A SURVEY ON PRODUCT REVIEW SENTIMENT ANALYSIS

Computer Engineering

BOW stands by Bag-of-words is the most popular way to model text in statistical machine learning approaches in sentiment analysis. However, the performance of BOW sometimes remains limited due to some fundamental deficiencies in handling the polarity shift problem. We propose a model called dual sentiment analysis (DSA), to address this problem for sentiment classification. Data mining technique is the process of turning raw data into useful information. The main use of data mining is to fetch the required data and extract useful information from the data and to interpret the data. Traditional system, BOWs model is used along with DSA in order to classify the reviews as positive, negative and neutral. However, the performance of Bag of Words sometimes remains limited due to some fundamental deficiencies in handling the polarity shift problem. Proposed system uses a dictionary based classification for accurately classifying the reviews as negative, positive and neutral. To enhance the accuracy in the classification of neutral reviews, Support Vector Machine algorithm is implemented. Product owner and the user both can identify the quality of the product based on the sentiment graph that is generated based on the reviews for each of the product. A relative study of the sentiment graphs is performed in order to improve the efficiency of visual representation.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.