Sentiment Analysis Challenges of Informal Arabic Language (original) (raw)
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Arabic-English Sentiment Analysis-An Empirical Studypaper ID 27
The Web 2.0 refers to the second generation of World Wide Web (WWW). Web 2.0 allows Internet users to collaborate and share information online, and therefore create large virtual societies. Web 2.0 includes social network sites, Wikis, Blogs, Web services, podcasting, Multimedia sharing services ...etc. Arab users of social network sites (Facebook and Twitter) generate daily a large volume of Arabic and English textual reviews related to different social, political and scientific subjects. These reviews could be about different products, political events, sport teams, economics, video clips, restaurants, books, actors/actress, new films and songs, universities ...etc. This large volume of different Arabic and English textual reviews cannot be analyzed manually. Therefore sentiment analysis is used to identify sentiments with their subjectivity from this huge volume of reviews. In order to conduct this study a small dataset consisting of 4,050 Arabic and English reviews were collected. Three polarity dictionaries were also created (Arabic, English, and Emoticons). The collected dataset and those dictionaries were used to conduct a comparison between two free online sentiment analysis tools (SocialMention
Arabic / English Sentiment Analysis: An Empirical Study
The Web 2.0 refers to the second generation of World Wide Web (WWW). Web 2.0 allows Internet users to collaborate and share information online, and therefore create large virtual societies. Web 2.0 includes social network sites, Wikis, Blogs, Web services, podcasting, Multimedia sharing services ...etc. Arab users of social network sites (Facebook and Twitter) generate daily a large volume of Arabic and English textual reviews related to different social, political and scientific subjects. These reviews could be about different products, political events, sport teams, economics, video clips, restaurants, books, actors/actress, new films and songs, universities ...etc. This large volume of different Arabic and English textual reviews cannot be analyzed manually. Therefore sentiment analysis is used to identify sentiments with their subjectivity from this huge volume of reviews. In order to conduct this study a small dataset consisting of 4,050 Arabic and English reviews were collected. Three polarity dictionaries were also created (Arabic, English, and Emoticons). The collected dataset and those dictionaries were used to conduct a comparison between two free online sentiment analysis tools (SocialMention
The tasks that falls under the errands that takes after Natural Language Processing approaches includes Named Entity Recognition, Information Retrieval, Machine Translation, and so on. Wherein Sentiment Analysis utilizes Natural Language Processing as one of the way to locate the subjective content showing negative, positive or impartial (neutral) extremity (polarity). Due to the expanded utilization of online networking sites like Facebook, Instagram, Twitter, Sentiment Analysis has increased colossal statures. Examination of sentiments helps organizations, government and other association to extemporize their items and administration in view of the audits or remarks. This paper introduces an Innovative methodology that investigates the part of lexicalization for Arabic Sentiment examination. The system was put in place with two principles rules– “equivalent to” and “within the text” rules. The outcomes subsequently accomplished with these rules methodology gave 89.6 % accuracy when tried on baseline dataset, and 50.1 % exactness on OCA, the second dataset. A further examination shows 19.5 % in system1 increase in accuracy when compared with baseline dataset.