Effective Sentiment Analysis Research Articles

Sentiment Classification Using Sentence-level Lexical Based Semantic Orientation of Online Reviews

Sentiment analysis is the process of extracting knowledge from the peoples’ opinions, appraisals and emotions toward entities, events and their attributes. These opinions greatly impact on customers to make their choices regarding online shopping, choosing events, products and entities. With the rapid growth of online resources, discussion groups, forums and blogs; people communicate through these means of internet on daily basis. As a result, the vast amount of new data in the form of customer reviews and opinions are being generated progressively. So it is desired to develop an efficient and effective sentiment analysis system for online customer reviews and comments. In this study, the rule based domain independent sentiment analysis method is proposed. The proposed method classifies subjective and objective sentences from reviews and blog comments. The semantic score of subjective sentences is extracted from SentiWordNet to calculate their polarity as positive, negative or neutral based on the contextual sentence structure. The results show the effectiveness of the proposed method and it outperforms the word level and machine learning methods. The proposed method achieves an accuracy of 97.8% at the feedback level and 86.6% at the sentence level.

Understanding what concerns consumers: a semantic approach to product feature extraction from consumer reviews

The Web has become an excellent source for gathering consumer opinions (more specifically, consumer reviews) about products. Consumer reviews are essential for retailers and product manufacturers to understand the general responses of customers to their products and improve their marketing campaigns or products accordingly. In addition, consumer reviews enable retailers to recognize the specific preferences of each customer, which facilitates effective marketing decisions. As the number of consumer reviews expands, it is essential and desirable to develop an efficient and effective sentiment analysis technique that is capable of extracting product features stated in consumer reviews (i.e., product feature extraction) and determining the sentiments (positive or negative semantic orientations) of consumers for these product features (i.e., opinion orientation identification). Product feature extraction is critical to sentiment analysis, because its effectiveness significantly affects the performance of opinion orientation identification, as well as the ultimate effectiveness of sentiment analysis. Therefore, this study concentrates on product feature extraction from consumer reviews. Specifically, we propose a semantic-based product feature extraction (SPE) technique that exploits a list of positive and negative adjectives defined in the General Inquirer to recognize opinion words semantically and subsequently extract product features expressed in consumer reviews. Using a prevalent product feature extraction technique and the SPE-GI technique (a variant of SPE) as performance benchmarks, our empirical evaluation shows that the proposed SPE technique outperforms both benchmark techniques.