(untitled) (International Journal of Engineering and Computer Science, 2018)
Sentiment Analysis is the process of computation identifying and categories the thought expressed by people in order to determine whether the writer’s attitude towards a particular product is positive or negative. The results of this analysis can be used in computing customer satisfaction metrics, marketing, contextual advertising, suggestion systems based on the user likes and rating, recommendation systems etc. It can be done on reviews, blogs, tweets, forums etc. Term Weighting is the process of assigning weights to the terms in the documents. Different term weighting schemes like Term Frequency, Term Frequency – Inverse Document Frequency and Binary Weighting methods are used on the reviews to weigh the sentiments words present in the reviews. It is done considering the terms as unigrams, bigrams, trigrams and Syntactic n-grams. The huge data presented to extract sentiments are unstructured in nature and they require processing like classification or clustering to get some meaningful information out of it. Supervised machine learning approaches like Naïve Bayes are used to classify the human sentiments present in the reviews as positive and negative.