Efficient feature extraction in sentiment classification for contrastive sentences

Sentiment Classification is a special task of Sentiments Analysis in which a text document is assigned into some category like positive, negative, and neutral on the basis of some subjective information contained in documents. This subjective information called as sentiment features are highly responsible for efficient sentiment classification. Thus, Feature extraction is essentially an important task for sentiment classification at any level. This study explores most relevant and crucial features for sentiment classification and groups them into seven categories, named as, Basic features, Seed word features, TF-IDF, Punctuation based features, Sentence based features, N-grams, and POS lexicons. This paper proposes two new sentence based features which are helpful in assigning the overall sentiment of contrastive sentences and on the basis of proposed features; two algorithms are developed to find the sentiment of contrastive sentences. The dataset of TripAdvisor is used to evaluate our proposed features. Obtained results are compared with several state-of-the-art studies using various features on the same dataset and achieve superior performance.