Polarity analisys od reviews based on the omission of asymetric sentences

In this paper, we present a novel approach to polarity analysis of product reviews which detects and removes sentences with the opposite polarity to that of the entire document (asymmetric sentences) as a previous step to identify positive and negative reviews. We postulate that asymmetric sentences are morpho-syntactically more complex than symmetric ones (sentences with the same polarity to that of the entire document) and that it is possible to improve the detection of the polarity orientation of reviews by removing asymmetric sentences from the text. To validate this hypothesis, we measured the syntactic complexity of both types of sentences in a multi-domain corpus of product reviews and contrasted three relevant data configurations based on inclusion and omission of asymmetric sentences from the reviews

Sentiment Classification of Online Customer Reviews and Blogs Using Sentence-level Lexical Based Semantic Orientation Method

ABSTRACT 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 ease their choices regarding online shopping, choosing events, products and entities. With the rapid growth of online resources, a vast amount of new data in the form of customer reviews and opinions are being generated progressively. Hence, sentiment analysis methods are desirable for developing efficient and effective analyses and classification of customer reviews, blogs and comments. The main inspiration for this thesis is to develop high performance domain independent sentiment classification method. This study focuses on sentiment analysis at the sentence level using lexical based method for different type data such as reviews and blogs. The proposed method is based on general lexicons i.e. WordNet, SentiWordNet and user defined lexical dictionaries for sentiment orientation. The relations and glosses of these dictionaries provide solution to the domain portability problem. The experiments are performed on various data sets such as customer reviews and blogs comments. The results show that the proposed method with sentence contextual information is effective for sentiment classification. The proposed method performs better than word and text level corpus based machine learning methods for semantic orientation. The results highlight that the proposed method achieves an average accuracy of 86% at sentence-level and 97% at feedback level for customer reviews. Similarly, it achieves an average accuracy of 83% at sentence level and 86% at feedback level for blog comment

Essential Speech and Language Technology for Dutch: Results by the STEVIN-programme

Computational Linguistics; Germanic Languages; Artificial Intelligence (incl. Robotics); Computing Methodologie