Critical Evaluation of Arabic Sentimental Analysis - IT Assignment Help

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Introduction Sentimental analysis are used widely in social media to understand opinions and emotions of user’s posts and reviews, the most morphological rich Semitic language is Arabic as it’s also one of the most popular languages on twitter. In the last few years not much research has focused on sentimental analysis in Dialectal Arabic as majority of research has focused on Modern standard Arabic. Most of the work focused on analysing English texts, as such expansion of sentimental analysis needs to reach other languages including Arabic. As stated by Santosh et. al. (2016) “Arabic text contains diacritics, representing most vowels, which affect the phonetic representation and give different meaning to the same lexical form.” This can make sentimental analysis challenging, in addition to other complications such as the unavailability of words with capital letters. Related research focused on classifying tweets by applying subjectivity, there has also been focus on classifying posts into categories such as news, events, opinions and deals. Different approaches have been applied from extracting features from texts and metadata to the use of generative models. Recent research has mainly concentrated on the retrieval of opinionated posts against specific subjects in terms of relevance using machine learning. Many translation systems rely on word stemming, datasets, and pre-processing, they also rely on sentiment analysis tools (Walid et. al. 2016). Related research includes testing of classifiers by providing individual judgements through the use of expert and volunteer labellers in order to evaluate the data as well as using it as a good source for future training (Amal 2016). As stated by Alok et. al. (2013) that they have “achieved relatively high precision, recall still requires improvement” this is in regards to sentimental analysis of micro-blogs in twitter. This research paper will analyse experiments of which have been conducted on sentiment analysis on Arabic in social media. This research will evaluate and compare the result for the experiments in order to reach good scientific conclusions on classifiers such as SVM, Naives Bayes and N-gram training models. This research paper will also evaluate lexicon, negation and emoticons as they require considerations in the sentiment analysis. There are challenges for example when translating English to Arabic there is a lack of resources due to the morphological and complex language being used. Emoticons play a part as they can cause confusion, for example some sentences may seem negative.

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