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

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

A Sentiment Analysis Using Fuzzy Support Vector Machine Algorithm

The Ministry of Communication and Information and the Ministry of BUMN of The Republic of Indonesia designed a mobile app “Peduli Lindungi” to be used to help the public and related government agencies in carrying out screening and tracing people's movement to stop the spread of Corona Virus Disease (Covid-19).The existence of a mobile app, “Peduli Lindungi” triggers abundant different sentiments from the Indonesian community, either positive or negative sentiments. Based on the positive sentiment, the government of the Republic of Indonesia may have some feedback about the aspects of the app that should be maintained. In contrast, negative sentiments can be used as initial points of the potential improvement of the mobile app. This study applies a Fuzzy Support Vector Machine (FSVM) model to classify the user's reviews on Peduli Lindungi Application. FSVM can classify customers’ reviews into two or more classes and relatively results in higher accuracy than other classification approaches. The results of this study indicate that the classification of reviews with FSVM produces quite good accuracy  with a value of 77%. A total correct prediction is 2192 reviews out of 2813 reviews

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

Fuzzy Sugeno Method for Opinion Classification Regarding Policy of PPKM and Covid-19 Vaccination

The Indonesian government has implemented various interventions to overcome the impact of the Covid-19 pandemic, including those written in Minister of Home Affairs Instructions on PPKM (Community Activities Restrictions Enforcement) and Covid-19 vaccination policies. This policy are not at least reaping the pros and cons, so it is necessary to monitor public opinion to be able to provide solutions or become an evaluation of future policies. The aim of this study is to determine the polarity of public opinion regarding PPKM and Covid-19 vaccinations policies on Twitter, as well as to determine the implementation of FIS Sugeno in classifying textual data. There are several stages carried out, i.e. data collection, data pre-processing, data labeling, data weighting, identification of membership functions, determination of fuzzy sets, formation of a classification system, and evaluation of classification results. In this study, the performance of FIS Sugeno in classifying tweets was quite good with an average accuracy of 89.13%. Meanwhile, public opinion regarding the PPKM and Covid-19 vaccination policies tends to be balanced with 36.92% of tweets classified as a positive sentiments, 22.85% being negative sentiments, and another 40.23% belonging to neutral sentiments

COVID-19 Vaccination and PPKM Policy with the Implementation of the Fuzzy Sugeno Method to Income Classification

This study aims to determine the implementation of Fuzzy Sugeno in classifying textual data obtained from Twitter so as to determine the polarity of public opinion regarding PPKM policies and Covid-19 vaccinations. This study uses primary data via Twitter related to COVID-19 vaccination and PPKM policies in Indonesia starting from February 9, 2021 to January 17, 2022. There are several stages carried out, namely data collection, data pre-processing, data labeling, data weighting. , identification of membership functions, determination of fuzzy sets, formation of classification systems, and evaluation of classification results. The results of this study explain that Fuzzy Sugeno's performance in classifying tweets is quite good with an average accuracy of 89.13%. Meanwhile, public opinion regarding PPKM policies and Covid-19 vaccinations tends to be balanced with 36.92% of tweets classified as positive sentiments, 22.85% negative sentiments, and another 40.23% classified as neutral sentiments. In addition, the fuzzy set that is formed based on the data observation method is very well done because it is able to adjust the frequency of the data in each category. This really helps improve the performance of the built classification system.

A Comparative Analysis of Opinion Mining and Sentiment Classification in Non-english Languages

In the past decade many opinion mining and sentiment classification studies have been carried out for opinions in English. However, the amount of work done for non-English text opinions is very limited.In this review, we investigate opinion mining and sentiment classification studies in three non-English languages to find the classification methods and the efficiency of each algorithm used in these methods. It is found that most of the research conducted for non-English has followed the methods used in the English language with onlylimited usage of language specific properties, such as morphological variations. The application domains seem to be restricted to particular fields and significantly less research has been conducted in cross domains. Keywords—Natural Language processing, Text mining, Machine Learning

MISNIS: an intelligent platform for Twitter topic mining

Twitter has become a major tool for spreading news, for dissemination of positions and ideas, and for the commenting and analysis of current world events. However, with more than 500 million tweets flowing per day, it is necessary to find efficient ways of collecting, storing, managing, mining and visualizing all this information. This is especially relevant if one considers that Twitter has no ways of indexing tweet contents, and that the only available categorization “mechanism” is the #hashtag, which is totally dependent of a user's will to use it. This paper presents an intelligent platform and framework, named MISNIS - Intelligent Mining of Public Social Networks’ Influence in Society - that facilitates these issues and allows a non-technical user to easily mine a given topic from a very large tweet's corpus and obtain relevant contents and indicators such as user influence or sentiment analysis. When compared to other existent similar platforms, MISNIS is an expert system that includes specifically developed intelligent techniques that: (1) Circumvent the Twitter API restrictions that limit access to 1% of all flowing tweets. The platform has been able to collect more than 80% of all flowing portuguese language tweets in Portugal when online; (2) Intelligently retrieve most tweets related to a given topic even when the tweets do not contain the topic #hashtag or user indicated keywords. A 40% increase in the number of retrieved relevant tweets has been reported in real world case studies. The platform is currently focused on Portuguese language tweets posted in Portugal. However, most developed technologies are language independent (e.g. intelligent retrieval, sentiment analysis, etc.), and technically MISNIS can be easily expanded to cover other languages and locations

A Consensus Approach to the Sentiment Analysis Problem Driven by Support-Based IOWA Majority

In group decision making, there are many situations where the opinion of the majority of participants is critical. The scenarios could be multiple, like a number of doctors finding commonality on the diagnose of an illness or parliament members looking for consensus on an specific law being passed. In this article, we present a method that utilizes induced ordered weighted averaging (IOWA) operators to aggregate a majority opinion from a number of sentiment analysis (SA) classification systems, where the latter occupy the role usually taken by human decision-makers as typically seen in group decision situations. In this case, the numerical outputs of different SA classification methods are used as input to a specific IOWA operator that is semantically close to the fuzzy linguistic quantifier ‘most of’. The object of the aggregation will be the intensity of the previously determined sentence polarity in such a way that the results represent what the majority think. During the experimental phase, the use of the IOWA operator coupled with the linguistic quantifier ‘most’ (math formula) proved to yield superior results compared to those achieved when utilizing other techniques commonly applied when some sort of averaging is needed, such as arithmetic mean or median techniques