انتخاب ويژگي براي شناسايي نويسنده در متون کوتاه برخط فارسي
محورهای موضوعی :
سمیه عارفی
1
(دانشجو)
محمد احسان بصیری
2
(مربی)
امید روزمند
3
(مربی)
کلید واژه: تحليل متن, تحليل سبک, استخراج ويژگي, انتخاب ويژگي و شناسايي نويسنده. ,
چکیده مقاله :
رشد فزايندهي استفاده از رسانه‌هاي اجتماعي و ارتباطات برخط به‌منظور بيان نظرات، تبادل عقايد و همچنين گسترش استفادهي کاربران فارسي زبان از اين ابزارها باعث افزايش متون فارسي در وب شده است. اين رشد چشمگير در کنار سوءاستفادههاي ناشي از ناشناس بودن نويسندهي نوشتهها نياز به سامانهي خودکار شناسايي نويسنده در اين زبان را بيش از پيش آشکار مي‌سازد. هدف از اين پژوهش، بررسي ويژگي‌هاي مؤثر در شناسايي نويسندگان نظرات فارسي توليد شده توسط خريداران گوشي و همچنین ارزیابی روش‌های نظارتی و غیرنظارتی می‌باشد. عواملي که در اين پژوهش بررسي ميشود شامل ويژگي‌هاي لغوي، نگارشی، معنايي، ساختاري، دستوري، مختص متن و مختص شبکه‌هاي اجتماعي است. پس از استخراج ويژگي‌هاي مذکور، انتخاب ويژگي‌هاي برتر توسط چهار الگوريتم همبستگي ويژگي، نسبت بهره، OneR و تحليل اجزاي اصلي آزمايش ميشود. در ادامه از الگوريتم‌هاي K-means، EM و خوشه‌بندي مبتني بر چگالي براي خوشه‌بندي و الگوريتم‌هاي شبکهي بيز، جنگل تصادفي و Bagging براي دستهبندي استفاده خواهد شد. ارزيابي الگوريتم‌هاي فوق بر روي نظرات فارسي مربوط به خريداران گوشي‌هاي سامسونگ نشان ميدهد که بهترين تشخيص در بين الگوريتم‌هاي خوشه‌بندي با دقت 16/59% مربوط به الگوريتم EM روي 15 ويژگي برتر انتخابي توسطOneR است درحالي‌که الگوريتم جنگل تصادفي به‌همراه نسبت بهره برای 90 ویژگی با دقت 57/79% بهترين کارايي را در بين الگوريتم‌هاي دستهبندي دارد. همچنين مقايسه‌ی ويژگي‌ها نشان داد که ويژگي‌هاي نگارشی بيشترين تأثير را در شناسايي نويسندهي متون کوتاه داشته و پس از آن به‌ترتيب ويژگي‌هاي لغوي ، مختص متن، مختص شبکه‌های اجتماعی، ساختاري، دستوري و معنایی قرار گرفتند.
The growing use of social media and online communication to express opinions, exchange ideas, and also the expanding use of of this platforms by Persian users has increased Persian texts on the Web. This remarkable growth, along with abusive use of the writer's anonymity, reveals the need for the author's automatic identification system in this language. In this research, the purpose of the study is to investigate the factors affecting the identification of authors of Persian reviews produced by cell-phone buyers and also to evaluate supervised and unsupervised methods. The factors considered in this research include lexical, syntactic, semantic, structural, grammatical, text-specific, and specific to social networks. After extracting these features, selecting the best features is tested by four algorithms including feature correlation, gain ratio, OneR, and principal components analysis. In the following, K-means, EM and density-based clustering will be used for clustering and Bayesian network, random forest, and Bagging will be used for categorization. The evaluation of the above algorithms on Persian comments of Samsung phone buyers indicates that the best performance among the clustering algorithms is 59/16% obtained by the EM algorithm on top-15 features selected by OneR, while the random forest algorithm using top-90 features selected by gain ratio with 79/57% achieves the best performance among the classification algorithms. Also, the comparison of features showed that syntactic features had the most effect on the identification of the author of short texts, and then, lexical, text-specific, specific to social networks, structural, grammatical and semantic features, respectively.
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