A Fuzzy Logic in Election Sentiment Analysis: Comparison Between Fuzzy Naïve Bayes and Fuzzy Sentiment using CNN

Sentiment analysis is an analysis with an objective to identify like, dislike, comments, opinion, or feedback on certain content which will be categorized into positive, negative, or neutral. In general selection, sentiment analysis widely known to be used to predict the winner on election process. This method tries to dig the people sentiment on their governor candidates during election, whether it’s positive, negative, or neutral opinion. The output of the positive sentiment is related to people acceptance towards one of the election nominee. That statement usually applied as a base reference for determining the result of the election process. In sentiment analysis, the importance of its fuzzy logics must be considered. Each of the people statement is assumed to have the level of positive, negative, or neutral percentage. The concept of fuzzy logic is developed and applied on one of this text mining method. This research is focusing on comparison analysis and fuzzy logic application in sentiment analysis method. Two method which discussed in this research are Fuzzy Naïve Bayes and Sentiment Fuzzy with convolutional neural network. This research is applied on PILKADA of Solo and Medan district case study. The data of the people opinion are acquired from twitter and collected on September 2020 to December 2020. The two methods which mentioned before are implemented on the acquired data and the output of these method application then compared. The conclusion of this research suggest that different approach will resulting in different output

Using Fuzzy Sentiment Computing and Inference Method to Study Consumer Online Reviews

As a new type of word-of-mouth information, online consumer reviews possess critical information regarding consumer‘s concerns and their experience with the product or service. Such information is considered essential to firms‘ business intelligence which can be utilized for the purpose of production recommendation, personalization, and better customer understanding. This paper considers the problem of online reviews sentiment mining based on the theory of consumer psychology and behavior. Given the fuzzy attribute nature of the online reviews, we have established fuzzy group bases of consumer psychology. Four fuzzy bases, including features, sense, mood and evaluation, are established. The consumer attitude elements are reflected by natural language reviews. A fuzzy sentiment computing algorithm of online reviews for consumer sentiment is developed, and a fuzzy rule base is also presented based on consumer decision-making process. Finally it shows by means of an experiment that the proposed approach is very well suited as an analysis tool for the online reviews sentiment mining problem

Sentiment Analysis Using Context Based Fuzzy Linguistic Hedges

Sentiment analysis refers to the inference of people’s views, positions and attitudes in their written or spoken te ts. We Present Context Based Fuzzy Linguistic Hedges, a novel approach for sentiment analysis which has proven effective both for regular texts and texts with a high degree of noise. We have proposed novel function that emulate the effect of different linguistic hedges by using fuzzy function and incorporated them in the sentiment classification task. Our paper using SentiWordNet Tool for determining the initial sentiment value

Analytical Marketing with Collective Perception

Social networks, forums and blogs are widely considered as a valuable source of information for many applications and in different domains. Being able to extract, analyze and use the knowledge, opinions and sentiments the users share on the Web can become a competitive advantage for any company or organization. Specifically, information about the feelings and the opinions of the users of a Web community with respect to a product or a service can be useful for marketing.  In this context, the concept of collective perception is gaining momentum as a way to process, evaluate and quantify the perception and the sentiment that a community of users share about a given phenomenon. In this work, we propose an approach, based on Fuzzy Logic and Sentiment Analysis techniques, which allows to evaluate, also in a quantitative manner, the collective perception of a Web community with respect to a specific product or service. Keywords: Collective Perception; Analytical Marketing; Fuzzy Logic; Sentiment Analysis

CROSA: Context-aware cloud service ranking approach using online reviews based on sentiment analysis

[EN] The explosion of cloud services over the Internet has raised new challenges in cloud service selection and ranking. The existence of a great variety of offered cloud services made the users think deeply about the most appropriate services that meet their needs and at the same time are adaptable to their context. Nowadays, online reviews are used for the purpose of enhancing the effectiveness of finding useful product information, having impact on the consumers' decision-making process. In this context, the current paper suggests a context-aware cloud service ranking approach using online reviews and based on sentiment analysis (CROSA). Its main objective is to ease the cloud service selection. The CROSA approach analyzes sentiments associated with service measurement index (SMI)-based service properties for each alternative cloud service. Moreover, it enhances the cloud service decision-making by supporting fuzzy sentiments through the intuitionistic fuzzy set theory and PROMETHEE II. The experimental results presented in this paper show that this approach is efficient and performing.Ben-Abdallah, E.; Boukadi, K.; Lloret, J.; Hammami, M. (2021). CROSA: Context-aware cloud service ranking approach using online reviews based on sentiment analysis. Concurrency and Computation: Practice and Experience. 33(7):1-16. https://doi.org/10.1002/cpe.5358S11633

A fuzzy approach to text classification with two-stage training for ambiguous instances

Sentiment analysis is a very popular application area of text mining and machine learning. The popular methods include Support Vector Machine, Naive Bayes, Decision Trees and Deep Neural Networks. However, these methods generally belong to discriminative learning, which aims to distinguish one class from others with a clear-cut outcome, under the presence of ground truth. In the context of text classification, instances are naturally fuzzy (can be multi-labeled in some application areas) and thus are not considered clear-cut, especially given the fact that labels assigned to sentiment in text represent an agreed level of subjective opinion for multiple human annotators rather than indisputable ground truth. This has motivated researchers to develop fuzzy methods, which typically train classifiers through generative learning, i.e. a fuzzy classifier is used to measure the degree to which an instance belongs to each class. Traditional fuzzy methods typically involve generation of a single fuzzy classifier and employ a fixed rule of defuzzification outputting the class with the maximum membership degree. The use of a single fuzzy classifier with the above fixed rule of defuzzification is likely to get the classifier encountering the text ambiguity situation on sentiment data, i.e. an instance may obtain equal membership degrees to both the positive and negative classes. In this paper, we focus on cyberhate classification, since the spread of hate speech via social media can have disruptive impacts on social cohesion and lead to regional and community tensions. Automatic detection of cyberhate has thus become a priority research area. In particular, we propose a modified fuzzy approach with two stage training for dealing with text ambiguity and classifying four types of hate speech, namely: religion, race, disability and sexual orientation - and compare its performance with those popular methods as well as some existing fuzzy approaches, while the features are prepared through the Bag-of-Words and Word Embedding feature extraction methods alongside the correlation based feature subset selection method. The experimental results show that the proposed fuzzy method outperforms the other methods in most cases

A Fuzzy Logic in Election Sentiment Analysis: Comparison Between Fuzzy Naïve Bayes and Fuzzy Sentiment using CNN

Sentiment analysis is an analysis with an objective to identify like, dislike, comments, opinion, or feedback on certain content which will be categorized into positive, negative, or neutral. In general selection, sentiment analysis widely known to be used to predict the winner on election process. This method tries to dig the people sentiment on their governor candidates during election, whether it’s positive, negative, or neutral opinion. The output of the positive sentiment is related to people acceptance towards one of the election nominee. That statement usually applied as a base reference for determining the result of the election process. In sentiment analysis, the importance of its fuzzy logics must be considered. Each of the people statement is assumed to have the level of positive, negative, or neutral percentage. The concept of fuzzy logic is developed and applied on one of this text mining method. This research is focusing on comparison analysis and fuzzy logic application in sentiment analysis method. Two method which discussed in this research are Fuzzy Naïve Bayes and Sentiment Fuzzy with convolutional neural network. This research is applied on PILKADA of Solo and Medan district case study. The data of the people opinion are acquired from twitter and collected on September 2020 to December 2020. The two methods which mentioned before are implemented on the acquired data and the output of these method application then compared. The conclusion of this research suggest that different approach will resulting in different outpu

Automatic domain ontology extraction for context-sensitive opinion mining

Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline

The sentiment-analysis algorithm of social networks text resources based on ontology

In this paper the features of semantic and sentiment analysis of textual data of social networks are presented, and an original model and algorithm for sentiment analysis of textual fragments of social networks using fuzzy linguistic ontology are proposed. This approach involves the use of various subgraphs of fuzzy ontology when considering texts of various subject areas with regard to contexts. In addition, the algorithm involves the assessment of the sentiment scores of individual syntagmatic structures into which the analyzed text fragments are divided. It also presents the results of experiments comparing the efficiency of the developed algorithm with a group of existing approaches in analyzing text fragments on the example of data from the social network VKontakte