Sentiment Analysis Using Deep Learning Techniques: A Review <s> A. Sentiment Analysis <s> The management of unprofitable customer relationships and in particular their abandonment is a topic that has received increasing interest in both managerial decision making and academic research. However, little is known about the attitudinal and behavioral reactions that companies should expect from current and potential customers in response to such strategies. Based on an online study of 773 customers, we show that the majority of current/potential customers react with affective or cognitive responses toward unprofitable customer abandonment, whereas salient behavioral attitude components are less frequent. In addition, we show that different attitude components lead to different consequences for the abandoning firm and that the salience of behavioral attitude components tends to be associated with more negative reactions. Finally, we show that the potential negative consequences of unprofitable customer abandonment for current customers can be compensated for by improvements in core service quality. <s> BIB001 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> A. Sentiment Analysis <s> The rapid growth of the World Wide Web has increased the online communication. The use of social networking sites is one of the approaches for putting views of user. In order to improve the textual methods of communication such as tweets, blogs and chat; it is needed to analyze the emotion of user by studying the input text and emotions of the user. Much of the current work in this area of emotion determination from text has typically focused on detecting the polarity of sentiment (positive/ negative/ neutral).This paper presents a novel approach for emotion estimation from the text entered by user on social networking sites with direct word or indirect emotions like emoji or smiley faces. The work proposed in this paper uses affective words and sentence context analysis methods for emotion determination. Also for helping the users to effectively express their emotion. On the basis of entered emotion by user it produces the result according to highest emotion classification. For designing the system some literature is review which provides the idea about previous work as well as comparison for improvement. <s> BIB002 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> A. Sentiment Analysis <s> Based on the methods of the traditional topic-based text classification, machine learning method was performed to the coarse-grained sentiment classification of reviews. Sentiment classification involved a lot of problems. In this paper, the sentiment Vector Space Model (s-VSM) was used for text representation to solve data sparseness. In addition, the critical issues of the sentiment classification, i.e. the selection of classification algorithms, the determination of feature selection method and the selection of feature dimension, are verified by experiments. Furthermore, in order to consider the entire corpus contribution of features and each category contribution of features, the feature selection method of Chi-square Difference between the Positive and Negative Categories (CDPNC) was proposed. It combined DF with CHI and had the better performance. Experiments showed that the Macro-F and Micro-F achieved 90.18% and 90.08% respectively. <s> BIB003
Sentiment analysis refers to the management of sentiments, opinions, and subjective text BIB002 . Sentiment analysis provide the comprehension information related to public views, as it analyze different tweets and reviews. It is a verified tool for the prediction of many significant events such as box office performance of movies and general elections  . Public reviews are used to evaluate a certain entity, i.e., person, product or location and might be found on different websites like Amazon and Yelp. The opinions can be categorized into negative, positive or neutral. The purpose of sentiment analysis is to automatically determine the expressive direction of user reviews BIB003 . The demand of sentiment analysis is raised due to increase requirement of analyzing and structuring hidden information which comes from the social media in the form of unstructured data BIB001 .