A comprehensive study on sentiment of Bengali text

Sentiment Analysis is one of the most important and challenging research topic in the field of natural language processing and opinion mining. In this article, six different approaches are discussed to determine the actual sentiment of the sentence and analyzed their performances. In parts of speech ratio method, the Parts of Speech (POS) of the queries are tagged and the POS ratio and the hamming distance between positive classifier and query and negative classifier and query are computed. To detect the sentiment more accurately, cosine similarity using TF-IDF is applied which is calculated by computing TF, DF and IDF and calculate positive vector, negative vector and query vector. In Cosine similarity using custom TF-IDF, custom POS tagger is used and TF, DF and IDF are computed. Another method with Naïve Bayes model using Uni-gram & stammer also gives good performance. In this approach, prior probability and conditional probability are calculated and the root words of the words are extracted. Naïve Bayes model using Bi-gram, stammer and normalizer is better than the other models. The last method discussed is Word Embedding with Hellinger PCA which presents the idea of word co-occurrence matrix and Skip-Gram to determine the actual contexts of the words, Hellinger PCA to determine most similar words and generate a sliding window of most probable context words around each word.