| { | |
| "paper_id": "U07-1006", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T03:08:58.264491Z" | |
| }, | |
| "title": "TAT: an author profiling tool with application to Arabic emails", | |
| "authors": [ | |
| { | |
| "first": "Dominique", | |
| "middle": [], | |
| "last": "Estival", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "[email protected]" | |
| }, | |
| { | |
| "first": "Tanja", | |
| "middle": [], | |
| "last": "Gaustad", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "[email protected]" | |
| }, | |
| { | |
| "first": "Son", | |
| "middle": [ | |
| "Bao" | |
| ], | |
| "last": "Pham", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "[email protected]" | |
| }, | |
| { | |
| "first": "Will", | |
| "middle": [], | |
| "last": "Radford", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "[email protected]" | |
| }, | |
| { | |
| "first": "Ben", | |
| "middle": [], | |
| "last": "Hutchinson", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "This paper reports on the application of the Text Attribution Tool (TAT) to profiling the authors of Arabic emails. The TAT system has been developed for the purpose of language-independent author profiling and has now been trained on two email corpora, English and Arabic. We describe the overall TAT system and the Machine Learning experiments resulting in classifiers for the different author traits. Predictions for demographic and psychometric author traits show improvements over the baseline for some of the author traits with both the English and the Arabic data. Arabic presents particular challenges for NLP and this paper describes more specifically the text processing components developed to handle Arabic emails.", | |
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| "abstract": [ | |
| { | |
| "text": "This paper reports on the application of the Text Attribution Tool (TAT) to profiling the authors of Arabic emails. The TAT system has been developed for the purpose of language-independent author profiling and has now been trained on two email corpora, English and Arabic. We describe the overall TAT system and the Machine Learning experiments resulting in classifiers for the different author traits. Predictions for demographic and psychometric author traits show improvements over the baseline for some of the author traits with both the English and the Arabic data. Arabic presents particular challenges for NLP and this paper describes more specifically the text processing components developed to handle Arabic emails.", | |
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| "section": "Abstract", | |
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| "text": "The goal of the TAT project is to develop a languageindependent Text Attribution Tool (TAT) which can provide information on authors for a variety of document types and a range of languages. In the first implementation, the TAT has been developed for profiling the authors of email messages in English and Arabic, with other languages to be added in the future. (Estival et al., 2007) describes the Machine Learning experiments and the results obtained for the English email data. In this paper, we will focus on the results obtained for Arabic emails and will describe the aspects of the TAT which are particular to the processing of our Arabic email data.", | |
| "cite_spans": [ | |
| { | |
| "start": 362, | |
| "end": 384, | |
| "text": "(Estival et al., 2007)", | |
| "ref_id": "BIBREF18" | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "We first introduce the two tasks of author attribution and author profiling in Section 2. In Section 3, we describe the data set on which our tool was trained and tested. Section 4 contains an overall description of the TAT system, followed by a more specific description of the processing steps required for Arabic emails in Section 5. After presenting the general experimental setup in Section 6, we report on the results achieved for several demographic and psychometric traits in Section 7 and propose our general conclusions in Section 8.", | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
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| "text": "The ability to recognise the identity or certain characteristics of authors automatically from texts has a number of potential applications. Author identification and author profiling can provide valuable information for marketing intelligence (Glance et al., 2005) , while the rapidly growing field of sentiment analysis and classification (Oberlander and Nowson, 2006 ) is another application where profiling can make a contribution. Also, author profiling forensics may be helpful in narrowing the choice of potential authors when identifying the source of a threat Argamon et al., 2005; Abbasi and Chen, 2005a) . Author attribution is the task of deciding for a given text which author, usually from a predefined set of authors, has written it. Historically, author identification has its roots in literature, with studies of the Bible (Friedmann, 1997) , Shakespeare (Ledger and Merriam, 1994) or the Federalist Pa-pers (Mosteller and Wallace, 1964) . Recently, author identification has also been applied to more informal texts, such as emails (de Vel, 2000; de Vel et al., 2001; , newsgroup messages (Zheng et al., 2003; Zheng et al., 2006) or blog entries Oberlander and Nowson, 2006) . Author profiling is the task of predicting one or more traits for the author of a text and the author profile consists of the set of traits predicted for that author. A major difference between author profiling and author attribution is that it is possible to predict author traits even when the training data does not contain any document by the actual author. Another difference is that greater accuracy can be expected for author profiling when the training data is made up of more authors, because the models for each trait are expected to be more robust. The accuracy of author identification, on the other hand, can be expected to decrease if the number of potential authors is increased.", | |
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| "text": "Abbasi and Chen, 2005a)", | |
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| "text": "(de Vel, 2000;", | |
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| "section": "Author attribution and author profiling", | |
| "sec_num": "2" | |
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| "text": "In (Estival et al., 2007) , we presented our work on author profiling for English emails and discussed the literature on previous research in that area for English texts. For Arabic texts, very few studies have been published in the area of author attribution; the only work we know of can be found in (Abbasi and Chen, 2005b) and (Abbasi and Chen, 2005a) . We are not aware of any work on author profiling for Arabic.", | |
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| "section": "Author attribution and author profiling", | |
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| "text": "For author attribution in Arabic, Abbasi and Chen (2005a) apply two different machine learners to a dataset of Arabic forum messages by 20 authors (with 20 messsages per author). The feature set comprises lexical, syntactic, structural and contentspecific features, including a number of features specifically tailored to Arabic. These relate mostly to inflection (counting roots rather than inflected words), word length (adjusting the range for the Arabic word length distribution to reflect the fact that Arabic words tend to be shorter than English ones) and elongation dashes (excluding them from word length measurements, but tracking their usage). The main conclusion reached is that using an SVM classifier with all the features achieves the best accuracy on their data set, but that the overall performance is lower than for English. Abbasi and Chen (2005b) use the same approach, and in addition include an in-depth comparison between the feature sets and results for English and Arabic on forum messages.", | |
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| "section": "Author attribution and author profiling", | |
| "sec_num": "2" | |
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| "text": "The data collected for this project and used for training the TAT consists of two sets of emails from 1033 English speakers and from 1030 Arabic speakers. The English data set contains emails written by both native and non-native speakers of English: native speakers of US English, native speakers of Australian/NZ English, native speakers of Spanish from the US and native speakers of Egyptian Arabic from Egypt. The Arabic data set contains emails written by native speakers of Egyptian Arabic, as described in more detail below. In the rest of this section, we focus on the data collection and validation processes for Arabic emails.", | |
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| "section": "Data", | |
| "sec_num": "3" | |
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| "text": "For the Arabic email data set, the collection was conducted in Egypt and all the writers were native speakers of Egyptian Arabic. Compared with the English email data set, a special feature is the encoding of the input for Arabic email. The widespread use of the Internet and even more of text messages via mobile phones without the possibility of Arabic script input has led to the use of the Latin alphabet and the development of some transliteration conventions in the Arabic speaking world. Even though Arabic keyboards are now more common, people still sometimes write email using a Latin keyboard. However, there are no strict rules for the conversion of Arabic script into Latin characters and the way of writing emails with a Latin keyboard varies greatly according to dialect or country, and even across individuals. Table 1 gives an overview of the number of authors, number of emails and number of words for both the Arabic and English data set. We also include the proportion of emails in Arabic script and in Latin script for the Arabic data.", | |
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| "section": "Data Collection", | |
| "sec_num": "3.1" | |
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| "text": "The data collection process for the Arabic data differed slightly from the process described in (Estival et al., 2007) for the collection of the English data, in that the respondents had to come to a central collection location. Nevertheless, the recruitment pro- cess also included notification of privacy and the assurance that their identity would be protected. The respondents agreed to fill out a web questionnaire to provide demographic and psychometric information about themselves and to donate at least ten email messages. Demographic traits cover basic demographic information about the author, namely age, gender, and level of education. The psychometric traits of the Arabic collection are based on a customized version of the short Eysenck Personality Questionnaire Revised (EPQR-S) (Francis et al., 2006) , consisting of 51 questions. These questions aim to identify four psychometric traits: extraversion, lie (or social desirability), neuroticism (or emotionality), and psychoticism (or toughmindedness).", | |
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| "text": "After completing the questionnaire, the writers either composed new email messages which they then sent to their recipients and also forwarded to the data collection email address, or directly forwarded previously sent emails, e.g. from their email client \"SentBox\".", | |
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| "text": "We collected at least 10 emails from each author, for a total number of 2000 words per author in the Arabic data set. Research has shown that the more complex morphology of Arabic (combined with a rich vocabulary) leads to a higher degree of inherent sparseness in Arabic data compared to similar English data. This suggests that larger amounts of data are needed for statistical Natural Language Processing (NLP) applications in Arabic (Goweder and Roeck, 2001) . Therefore, while the minimum was set at 1000 words per writer for the English data, it was 2000 words per writer for the Arabic data.", | |
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| "text": "The email messages were checked manually to filter out erroneous content such as foreign language emails or forwarded chain letters and to ensure con-sistency and accuracy of the documents in the corpus. As with any data collection of email, plagiarism and copying were issues that required careful checking of all the data received and we developed a plagiarism detecter to reject emails which had already been submitted.", | |
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| "section": "Data Validation", | |
| "sec_num": "3.2" | |
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| "text": "For both the English and the Arabic data collections, a minimum number of 5 lines per email had been set. Because Arabic writers often do not use new lines or new paragraphs, this had to be measured visually on the screen for the Arabic data.", | |
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| "text": "In summary, the final Arabic data set contains a combined total of 8028 email messages, from 1030 writers who met the following criteria: 1) a valid questionnaire was received for each author; 2) there are at least 5 valid email messages for the author; and 3) the total word count for that author's valid email messages is at least 2000 words. Figure 1 gives a high-level overview of the TAT system. The system includes several data repositories and a number of components for deriving features and for building classifiers. While the current focus is on processing email input in English and in Arabic, the underlying processing architecture is language independent and will be extended to other types of documents and to other languages.", | |
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| "text": "The modular processing architecture is organized around a chain of processing modules. This chain allows flexible experimentation with various processing modules. At the same time, it provides a robust software framework that promotes reuse and supports flexible deployment options by connecting specific modules together for the task at hand.", | |
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| "text": "Each processing module consumes objects from its input, such as documents, and emits objects containing the analysis of the input objects. The analysis of a document is represented in stand-off anno- The process is data-driven in the sense that the output of each processing module depends on its input rather than on the way the module is combined with other modules in the chain. This enables processing modules to be reused in different processing chains and in different control environments as long as the input requirements are met.", | |
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| "text": "After the initial data collection and validation, several processing steps are needed, including character encoding normalisation, document structure parsing, text processing and linguistic analysis. The results of this processing provides the input to the feature extraction phase. A more extensive description of the processing steps for the English data can be found in (Estival et al., 2007) .", | |
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| "section": "Arabic Text processing", | |
| "sec_num": "5" | |
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| "text": "Arabic emails present a number of challenges for NLP, including different ways of writing Arabic in Latin script (so-called \"franco-arabic\"); typical spelling variants in the Egyptian dialect of Arabic and possible spelling normalisation; morphol-ogy; English loanwords, spelling errors, and typos.", | |
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| "text": "The Arabic processing modules perform the following functions:", | |
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| "section": "Arabic Text processing", | |
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| "text": "1. Language and character-set identification. The language is tagged as either English or Arabic and the script as either \"roman\" or \"arabic\", using character-based language models.", | |
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| "section": "Arabic Text processing", | |
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| "text": "2. Document structure parsing. This stage distinguishes the text of the email written by the author from other types of elements (adverts, signatures, reply lines, or quoted text). 3. Tokenisation. The input text is split into paragraphs, sentences and words. Words are mainly strings of alphanumeric characters, with a few major exceptions for transliterated Arabic: some two character sequences, e.g. \" 3' \", can indicate a single Arabic glyph, namely .", | |
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| "text": "4. Character set normalisation. In order to achieve a normalised, unambiguous input for Arabic and Latin scripted texts, words are converted to ASCII only characters using the Buckwalter transliteration scheme (Buckwalter, 2000; Buckwalter, 2002) . The Buckwalter scheme is very commonly used in NLP for Arabic because it represents Arabic orthography strictly on a one-to-one basis (unlike common romanization schemes that add morphological information not actually expressed in the Arabic script).", | |
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| "text": "Informal written Arabic often contains non-standard spellings (Buckwalter, 2004; Goweder and Roeck, 2001) . We have therefore normalised the spelling based on common spelling variations in Egyptian Arabic. Examples include word final becoming or word final becoming . Also, depending on the context, can take different chairs or appear by itself on the line.", | |
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| "section": "Spelling normalisation.", | |
| "sec_num": "5." | |
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| { | |
| "text": "We normalise the hamza chair to .", | |
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| "section": "Spelling normalisation.", | |
| "sec_num": "5." | |
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| "text": "6. Morphological analysis. By comparing the normalised version of a given word with dictionaries of prefixes, suffixes and clitics, linguistic features such as number and person are added, and the remainder of the word is tagged as a stem. The root letters of the stem are then predicted using simple linguistic heuristics, e.g. long vowels are less likely to be root letters.", | |
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| "section": "Spelling normalisation.", | |
| "sec_num": "5." | |
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| "text": "7. Lexicon taggers. The following word classes are currently tagged: conjunctions, prepositions, pronouns, discourse particles, interrogative pronouns, English loan words, colloquial Egyptian words, and frequent roots.", | |
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| "section": "Spelling normalisation.", | |
| "sec_num": "5." | |
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| { | |
| "text": "8. Named Entity Recognition. Named entities which are not language-dependent are tagged. These include numeric dates, numeric times, phone numbers, email addresses and URLs.", | |
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| "text": "Recent years have seen an exponential increase in the use of statistical language processing techniques for a wide range of tasks. In particular, text and document classification problems greatly benefit from statistical approaches. Author profiling can be viewed as a type of document classification task, where the classes correspond to traits of the authors. These traits are arranged along various dimensions and the different options for each dimension are mutually exclusive. For example \"male\" and \"female\" are the possibilities for the gender dimension. For each dimension, the email and questionnaire data are used to construct classifiers, using a range of Machine Learning (ML) techniques.", | |
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| "section": "Experimental setup", | |
| "sec_num": "6" | |
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| "text": "Each document constitutes a single data instance for the purposes of the experiments. For each experiment, ten-fold cross-validation was used, so the results reported in Section 7 are on the entire data set. Once the best combination of ML classifiers, parameters and feature selection has been determined during development, that model is used to classify the test data to evaluate the performance for a given trait.", | |
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| "section": "Experimental setup", | |
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| "text": "We distinguish three different demographic and four psychometric traits in the experiments presented in this paper, namely age, gender, and level of education for the demographic traits, and extraversion, lie, neuroticism, and psychoticism for the psychometric traits (Eysenck and Eysenck, 1975) . This information is extracted from the questionnaires.", | |
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| "end": 295, | |
| "text": "(Eysenck and Eysenck, 1975)", | |
| "ref_id": "BIBREF19" | |
| } | |
| ], | |
| "ref_spans": [], | |
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| "section": "Traits and classes", | |
| "sec_num": "6.1" | |
| }, | |
| { | |
| "text": "For the traits taking numerical values, subjects were split into three classes based on the first and third quartiles. Table 2 summarizes the data distribution for each trait across these classes.", | |
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| "text": "A major difference with regard to the psychometric traits described in (Estival et al., 2007) lies in the fact that we used two different questionnaires for English and for Arabic. The International Personality Item Pool questionnaire (IPIP) (Buchanan et al., 2005) , which was used for collecting the English data, yields five psychometric traits: agreeableness, extraversion, neuroticism, conscientiousness, and openness (also referred to as the \"Big Five\") (Norman, 1963) . However the questionnaire for the Arabic data collection was based on the short form of the revised Eysenck Personality Questionnaire, the EPQR-S first developed for a study in Germany (Francis et al., 2006) , and the adaptation of the EPQ for Arabic (Abdel-Khalek and Eysenenck, 1983) . The EPQ (Eysenck and Eysenck, 1975) aims to analyze personality along four traits, namely extraversion, neuroticism, psychoticism, and lie. This entails that the Arabic results for the psychometric traits are not directly comparable to the English ones.", | |
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| "start": 460, | |
| "end": 474, | |
| "text": "(Norman, 1963)", | |
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| "text": "(Francis et al., 2006)", | |
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| "text": "(Abdel-Khalek and Eysenenck, 1983)", | |
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| "sec_num": "6.1" | |
| }, | |
| { | |
| "text": "Gender: Level of education: <25 691Male ( Table 2 : Traits and Classes, with frequencies in parentheses where applicable.", | |
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| { | |
| "start": 42, | |
| "end": 49, | |
| "text": "Table 2", | |
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| "section": "Age:", | |
| "sec_num": null | |
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| "text": "For each document, a feature vector is calculated. A feature is typically a descriptive statistic calculated from both the raw text and the annotations. For example, a feature might express the relative frequency of two different annotation types (e.g. number of words/number of sentences), or the presence or absence of an annotation type (e.g. Advert).", | |
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| "section": "Features", | |
| "sec_num": "6.2" | |
| }, | |
| { | |
| "text": "For the Arabic data, 518 features were calculated. These were divided into several subgroups shown in Table 3 . The main purpose of the groupings was to make more informed choices during the feature selection stage and to facilitate experimentation with various combinations of feature groups.", | |
| "cite_spans": [], | |
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| "sec_num": "6.2" | |
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| "text": "Character-level features cover features such as the frequency of punctuation characters or word length. Arabic-specific character features include information on spelling normalisation and Arabic special characters. Morphological features mainly encode information on number, person and gender markers, such as clitics or suffixes. Lexical features include certain POS tags (e.g. preposition) and whether a word is an English loanword or specific to the Egyptian dialect.", | |
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| "sec_num": "6.2" | |
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| "text": "Classifiers for predicting author traits from the linguistic features were trained using the WEKA toolkit (Witten and Frank, 2005) . During training, classifiers are created by the selection of sets of features for each trait, and classifier parameters are tuned through cross-validation. To evaluate and test the classifiers, new documents are given as input and existing classifiers are selected to predict author traits.", | |
| "cite_spans": [ | |
| { | |
| "start": 106, | |
| "end": 130, | |
| "text": "(Witten and Frank, 2005)", | |
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| "section": "Classification algorithms and feature selection", | |
| "sec_num": "6.3" | |
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| "text": "The machine learning algorithms tested include decision trees (J48 (Quinlan, 1993) , RandomForest (Breiman, 2001) ), lazy learners (IBk (Aha et al., 1991) ), rule-based learners (JRip (Cohen, 1995) ), Support Vector Machines (SMO (Keerthi et al., 2001 )), as well as ensemble/meta-learners (Bagging (Breiman, 1996) , AdaBoostM1 (Freund and Schapire, 1996) ). These algorithms were used in combination with feature selection methods based on either a feature subset evaluator together with a search method (consistency subset evaluator with a best-first search) or a single attribute evaluator with various numbers of attributes selected (\u03c7 2 , GainRatio, and InformationGain) (see chapter 10.8 in (Witten and Frank, 2005) for details).", | |
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| "start": 67, | |
| "end": 82, | |
| "text": "(Quinlan, 1993)", | |
| "ref_id": "BIBREF31" | |
| }, | |
| { | |
| "start": 98, | |
| "end": 113, | |
| "text": "(Breiman, 2001)", | |
| "ref_id": "BIBREF6" | |
| }, | |
| { | |
| "start": 136, | |
| "end": 154, | |
| "text": "(Aha et al., 1991)", | |
| "ref_id": "BIBREF3" | |
| }, | |
| { | |
| "start": 184, | |
| "end": 197, | |
| "text": "(Cohen, 1995)", | |
| "ref_id": "BIBREF11" | |
| }, | |
| { | |
| "start": 230, | |
| "end": 251, | |
| "text": "(Keerthi et al., 2001", | |
| "ref_id": "BIBREF25" | |
| }, | |
| { | |
| "start": 299, | |
| "end": 314, | |
| "text": "(Breiman, 1996)", | |
| "ref_id": "BIBREF5" | |
| }, | |
| { | |
| "start": 328, | |
| "end": 355, | |
| "text": "(Freund and Schapire, 1996)", | |
| "ref_id": "BIBREF21" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Classification algorithms and feature selection", | |
| "sec_num": "6.3" | |
| }, | |
| { | |
| "text": "The results shown here were computed on the Arabic email data set described in Section 3 using the different classifiers and general setup introduced in Section 6. Table 4 shows the results on all seven traits (demographic and psychometric), including the respective baseline associated with each separate classification task. We also state which settings (ML algorithm, feature set, and feature selection) were used to achieve the results reported. Education and gender are both binary classification tasks, whereas age uses three classes. All the psychometric traits are divided into three classes (see Section 6.1 and Table 2 for details on the exact split).", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 164, | |
| "end": 171, | |
| "text": "Table 4", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 621, | |
| "end": 628, | |
| "text": "Table 2", | |
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| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Results and discussion", | |
| "sec_num": "7" | |
| }, | |
| { | |
| "text": "The results show that for six out of the seven traits Table 4 : Results for all demographic and psychometric traits on Arabic email data.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 54, | |
| "end": 61, | |
| "text": "Table 4", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Results and discussion", | |
| "sec_num": "7" | |
| }, | |
| { | |
| "text": "tested for, classification is significantly 1 improved over the baseline. For education, virtually no improvement can be seen which is due to the extremely skewed data (as indicated by the very high baseline of 93.62%). Even though the baselines for the other demographic traits are also quite high, our system still achieves a better classification accuracy for age and gender than the majority baseline. The better result for gender can in part be explained by the fact that gender is morphologically marked in Arabic. One of the relevant constructions with respect to identifying an author's gender are predicative sentences with first person subjects. For example, in the Arabic equivalent of \"I am happy\", happy is morphologically marked as either feminine or masculine. Since our features include morphological information, our classifier detects gender differences very accurately. A more detailed analysis of the effects of each feature group on the prediction of gender (shown in Table 5 ) reveals that lexical features are also of great assistance. Table 6 shows the results for English and Arabic demographic traits that are directly comparable. This seems to confirm previous results showing that predicting author traits for Arabic is likely to be 1 Significance was tested using a \u03c7 2 test with p=0.01. more difficult than for English. One should not forget, however, that the baselines for all Arabic demographic traits are extremely high which means that little data is available for the minority classes.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 989, | |
| "end": 996, | |
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| }, | |
| { | |
| "start": 1059, | |
| "end": 1066, | |
| "text": "Table 6", | |
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| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Results and discussion", | |
| "sec_num": "7" | |
| }, | |
| { | |
| "text": "For the psychometric traits, we achieve similar improvements over the baseline as for English. This is particularly encouraging, as most research on Arabic author attribution has shown results for Arabic to be lower than for English. It seems that predicting a profile rather than an identity might be advantageous for Arabic, or at least a viable back-off option.", | |
| "cite_spans": [], | |
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| "section": "Results and discussion", | |
| "sec_num": "7" | |
| }, | |
| { | |
| "text": "From our results, two ML algorithms emerge as best performing for all traits examined, namely SMO and Bagging. Bagging seems to profit from feature selection whereas the Support Vector Machine based SMO algorithm does not show additional improvements when combined with feature selection. This differs slightly from our results with the English data set, where no clear conclusion could be drawn with regard to the usefulness of feature selection for different algorithms.", | |
| "cite_spans": [], | |
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| "section": "Results and discussion", | |
| "sec_num": "7" | |
| }, | |
| { | |
| "text": "An analysis of the results shows that the highest accuracy is achieved by including all available features, with the exclusion of a single feature group for age (arabicLexical) and for extraversion (arabic-Morphological). ", | |
| "cite_spans": [], | |
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| "section": "Results and discussion", | |
| "sec_num": "7" | |
| }, | |
| { | |
| "text": "We have presented some results of experiments to automatically predict author traits from email messages. This work is of interest for a number of potential applications, from marketing intelligence to sentiment analysis. The results presented in this paper were conducted on the Arabic subset of the email data we have collected (containing approximately 8028 emails). The experiments reported here were aimed at discovering how well a range of ML algorithms perform on our data set for three demographic and four psychometric author traits. Our results support the conclusions drawn in (Estival et al., 2007) that the chosen approach works well for author profiling and that using different classifiers in combination with a subset of available features can be beneficial for predicting single traits.", | |
| "cite_spans": [ | |
| { | |
| "start": 588, | |
| "end": 610, | |
| "text": "(Estival et al., 2007)", | |
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| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion and future work", | |
| "sec_num": "8" | |
| }, | |
| { | |
| "text": "Future work will include the extension of the TAT to other document types and other languages. For Arabic text processing, it might be fruitful to investigate a more sophisticated analysis of words into roots (Darwish, 2002; de Roeck and Al-Fares, 2000) .", | |
| "cite_spans": [ | |
| { | |
| "start": 209, | |
| "end": 224, | |
| "text": "(Darwish, 2002;", | |
| "ref_id": "BIBREF13" | |
| }, | |
| { | |
| "start": 225, | |
| "end": 253, | |
| "text": "de Roeck and Al-Fares, 2000)", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [], | |
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| "section": "Conclusion and future work", | |
| "sec_num": "8" | |
| } | |
| ], | |
| "back_matter": [ | |
| { | |
| "text": "This research was carried out within the framework of a BAA grant (IS-QD-2467). We would like to thank Judith Bishop for her important contribution to the project in successfully managing the data collection.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Acknowledgements", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Description all all features for Arabic arabicNamedEntities language independent named entities, such as URLs arabicChar Arabic character features arabicMorphological Arabic morphology features arabicLexical Arabic lexicon and word features ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "Feature group", | |
| "sec_num": null | |
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| "text": "TAT System Diagram tations and saved in a common structure called the Annotation Repository.", | |
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