Summary In the competing era of online industries, understanding customer feedback and satisfaction is one of the important concern for any business organization. The well‐known social media platforms like Twitter are a place where customers share their feedbacks. Analyzing customer feedback is beneficial, as it provides an advantage way of unveiling customer interests. The proposed system, namely Senti‐eSystem, aims at the development of sentiment‐based eSystem using hybridized Fuzzy and Deep Neural Network for Measuring Customer Satisfaction to assist business organizations for improving the quality of their services and products. The proposed approach initially deploys a Bidirectional Long Short Term Memory with attention mechanism to predict the sentiment polarity that is positive and negative, followed by Fuzzy logic approach to determine the customer satisfaction level, which further strengthens the capabilities of the proposed approach. The system achieves an accuracy of 92.86%, outperforming the previous state‐of‐art lexicon‐based approaches. Moreover, the effectiveness of the proposed system is also validated by applying the statistical test.

Abstract The sentiment analysis (SA) applications are becoming popular among the individuals and organizations for gathering and analysing user's sentiments about products, services, policies, and current affairs. Due to the availability of a wide range of English lexical resources, such as part‐of‐speech taggers, parsers, and polarity lexicons, development of sophisticated SA applications for the English language has attracted many researchers. Although there have been efforts for creating polarity lexicons in non‐English languages such as Urdu, they suffer from many deficiencies, such as lack of publically available sentiment lexicons with a proper scoring mechanism of opinion words and modifiers. In this work, we present a word‐level translation scheme for creating a first comprehensive Urdu polarity resource: “Urdu Lexicon” using a merger of existing resources: list of English opinion words, SentiWordNet, English–Urdu bilingual dictionary, and a collection of Urdu modifiers. We assign two polarity scores, positive and negative, to each Urdu opinion word. Moreover, modifiers are collected, classified, and tagged with proper polarity scores. We also perform an extrinsic evaluation in terms of subjectivity detection and sentiment classification, and the evaluation results show that the polarity scores assigned by this technique are more accurate than the baseline methods.

Abstract Of the many social media sites available, users prefer microblogging services such as Twitter to learn about product services, social events, and political trends. Twitter is considered an important source of information in sentiment analysis applications. Supervised and unsupervised machine learning‐based techniques for Twitter data analysis have been investigated in the last few years, often resulting in an incorrect classification of sentiments. In this paper, we focus on these issues and present a unified framework for classifying tweets using a hybrid classification scheme. The proposed method aims at improving the performance of Twitter‐based sentiment analysis systems by incorporating 4 classifiers: (a) a slang classifier, (b) an emoticon classifier, (c) the SentiWordNet classifier, and (d) an improved domain‐specific classifier. After applying the preprocessing steps, the input text is passed through the emoticon and slang classifiers. In the next stage, SentiWordNet‐based and domain‐specific classifiers are applied to classify the text more accurately. Finally, sentiment classification is performed at sentence and document levels. The findings revealed that the proposed method overcomes the limitations of previous methods by considering slang, emoticons, and domain‐specific terms.