A Survey

2020 ◽  
Vol 11 (1) ◽  
pp. 28-33
Author(s):  
Reshma Radheshamjee Baheti ◽  
Supriya Kinariwala
Keyword(s):  
Sentiment Analysis ◽  
Life Events ◽  
Language Processing ◽  
Social Networking Sites ◽  
Human Life ◽  
Discussion Forums ◽  
Huge Number ◽  
Textual Data ◽  
Human Stress ◽  
Social Media Network

Recently, human stress is rapidly increasing. The school-college students, job professionals, and many people those work under pressure. In last few decades, research is going on how to predict people under pressure or feeling relax with his/her duty. In survey it is evaluated, sentiment analysis will work to find emotions or feelings about their daily life. By analyzing social media network like Facebook, Twitter, and other networking sites where user can share personal feelings like happy, angry, stressed, relaxed, or any other emotion to express human life events or views regarding any topic. On social networking sites, a huge number of informal messages are posted every day, also blogs or discussion forums are also available. Emotions appear to be frequently vital in these texts for expressing friendship, and the presentation of social support as a part of opinions or view. In this article, a survey is done on existing techniques which are working to find sentiment analysis of textual data. In the textual data, the positive and negative sentences have to be found to check the emotions of the user. The survey also finds the natural language processing, the lexical parser, sentiment analysis, the classifier algorithm and some different kinds of Twitter datasets. It is found that 85% work completed on sentiment analysis and categorized the sentences as positive or negative.

scholarly journals A comparative review of the challenges encountered in sentiment analysis of Indian regional language tweets vs English language tweets

2018 ◽  
Vol 7 (2.21) ◽  
pp. 319
Author(s):  
Saini Jacob Soman ◽  
P Swaminathan ◽  
R Anandan ◽  
K Kalaivani
Keyword(s):  
Sentiment Analysis ◽  
Life Events ◽  
Social Networking Sites ◽  
Positive Emotions ◽  
English Language ◽  
Indian Languages ◽  
Regional Language ◽  
Comparative Review ◽  
Computational Research ◽  
Regional Languages

With the developed use of online medium these days for sharing views, sentiments and opinions about products, services, organization and people, micro blogging and social networking sites are acquiring a huge popularity. One of the biggest social media sites namely Twitter is used by several people to share their life events, views and opinion about different areas and concepts. Sentiment analysis is the computational research of reviews, opinions, attitudes, views and peoples’ emotions about different products, services, firms and topics through categorizing them as negative and positive emotions. Sentiment analysis of tweets is a challenging task. This paper makes a critical review on the comparison of the challenges associated with sentiment analysis of Tweets in English Language versus Indian Regional Languages. Five Indian languages namely Tamil, Malayalam, Telugu, Hindi and Bengali have been considered in this research and several challenges associated with the analysis of Twitter sentiments in those languages have been identified and conceptualized in the form of a framework in this research through systematic review.  

Summarizing Opinions with Sentiment Analysis from Multiple Reviews on Travel Destinations

2018 ◽  
Vol 9 (2) ◽  
pp. 111-120
Author(s):  
Argha Roy ◽  
Shyamali Guria ◽  
Suman Halder ◽  
Sayani Banerjee ◽  
Sourav Mandal
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Efficient Method ◽  
Text Analysis ◽  
Human Life ◽  
Analysis Techniques ◽  
Classification Technique ◽  
Textual Data ◽  
The Web

Recently, the web has been crowded with growing volumes of various texts on every aspect of human life. It is difficult to rapidly access, analyze, and compose important decisions using efficient methods for raw textual data in the form of social media, blogs, feedback, reviews, etc., which receive textual inputs directly. It proposes an efficient method for summarization of various reviews of tourists on a specific tourist spot towards analyzing their sentiments towards the place. A classification technique automatically arranges documents into predefined categories and a summarization algorithm produces the exact condensed input such that output is most significant concepts of source documents. Finally, sentiment analysis is done in summarized opinion using NLP and text analysis techniques to show overall sentiment about the spot. Therefore, interested tourists can plan to visit the place do not go through all the reviews, rather they go through summarized documents with the overall sentiment about target place.

scholarly journals A Novel Framework Using Neutrosophy for Integrated Speech and Text Sentiment Analysis

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1715
Author(s):  
Kritika Mishra ◽  
Ilanthenral Kandasamy ◽  
Vasantha Kandasamy W. B. ◽  
Florentin Smarandache
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Wide Spectrum ◽  
Neutrosophic Set ◽  
Text File ◽  
Neutrosophic Sets ◽  
Plain Text ◽  
Textual Data ◽  
Audio Files ◽  
Text Sentiment Analysis

With increasing data on the Internet, it is becoming difficult to analyze every bit and make sure it can be used efficiently for all the businesses. One useful technique using Natural Language Processing (NLP) is sentiment analysis. Various algorithms can be used to classify textual data based on various scales ranging from just positive-negative, positive-neutral-negative to a wide spectrum of emotions. While a lot of work has been done on text, only a lesser amount of research has been done on audio datasets. An audio file contains more features that can be extracted from its amplitude and frequency than a plain text file. The neutrosophic set is symmetric in nature, and similarly refined neutrosophic set that has the refined indeterminacies I1 and I2 in the middle between the extremes Truth T and False F. Neutrosophy which deals with the concept of indeterminacy is another not so explored topic in NLP. Though neutrosophy has been used in sentiment analysis of textual data, it has not been used in speech sentiment analysis. We have proposed a novel framework that performs sentiment analysis on audio files by calculating their Single-Valued Neutrosophic Sets (SVNS) and clustering them into positive-neutral-negative and combines these results with those obtained by performing sentiment analysis on the text files of those audio.

Summarizing Opinions with Sentiment Analysis from Multiple Reviews on Travel Destinations

2020 ◽  
pp. 310-320
Author(s):  
Argha Roy ◽  
Shyamali Guria ◽  
Suman Halder ◽  
Sayani Banerjee ◽  
Sourav Mandal
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Efficient Method ◽  
Text Analysis ◽  
Human Life ◽  
Analysis Techniques ◽  
Classification Technique ◽  
Textual Data ◽  
The Web

Recently, the web has been crowded with growing volumes of various texts on every aspect of human life. It is difficult to rapidly access, analyze, and compose important decisions using efficient methods for raw textual data in the form of social media, blogs, feedback, reviews, etc., which receive textual inputs directly. It proposes an efficient method for summarization of various reviews of tourists on a specific tourist spot towards analyzing their sentiments towards the place. A classification technique automatically arranges documents into predefined categories and a summarization algorithm produces the exact condensed input such that output is most significant concepts of source documents. Finally, sentiment analysis is done in summarized opinion using NLP and text analysis techniques to show overall sentiment about the spot. Therefore, interested tourists can plan to visit the place do not go through all the reviews, rather they go through summarized documents with the overall sentiment about target place.

Offline vs. Online Sentiment Analysis

2017 ◽  
Vol 7 (4) ◽  
pp. 1-18 ◽  
Author(s):  
Ritesh Srivastava ◽  
M.P.S. Bhatia
Keyword(s):  
Sentiment Analysis ◽  
Real Time ◽  
Language Processing ◽  
Real World ◽  
Social Networking Sites ◽  
The Real ◽  
Intelligent Decision Making ◽  
Real Time Analysis ◽  
Analysis Process ◽  
Intelligent Decision

Recently, the social networking sites (SNSs) have proven their immense power of prediction for predicting the results of the real-world events. However, for real-time monitoring of the world activities via microblogging site like Twitter, it is important to perform the sentiment analysis of online micro-texts in real-time to support fast and intelligent decision-making and hence to execute the appropriate actions in the real world in real-time. In this context, this paper discusses the online sentiment analysis process of online micro-texts in perspectives of the real-time analysis process. In addition, this paper argues the non-applicability of the classical time consuming Natural Language Processing (NLP) methods and the affinity of Machine Learning (ML) methods in performing the online sentiment analysis by contrasting it with offline sentiment analysis. Furthermore, it also formalized the online sentiment analysis process of online micro-texts by raising novel issues and proposing new performance measures for online sentiment analysis.

Topic features for machine learning-based sentiment analysis in Indonesian tweets

2019 ◽  
Vol 12 (1) ◽  
pp. 70-81 ◽  
Author(s):  
Hendri Murfi ◽  
Furida Lusi Siagian ◽  
Yudi Satria
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Analysis Data ◽  
Data Representation ◽  
Support Vector ◽  
Data Sets ◽  
Content Type ◽  
Textual Data ◽  
Standard Word

Purpose The purpose of this paper is to analyze topics as alternative features for sentiment analysis in Indonesian tweets. Design/methodology/approach Given Indonesian tweets, the processes of sentiment analysis start by extracting features from the tweets. The features are words or topics. The authors use non-negative matrix factorization to extract the topics and apply a support vector machine to classify the tweets into its sentiment class. Findings The authors analyze the accuracy using the two-class and three-class sentiment analysis data sets. Both data sets are about sentiments of candidates for Indonesian presidential election. The experiments show that the standard word features give better accuracies than the topics features for the two-class sentiment analysis. Moreover, the topic features can slightly improve the accuracy of the standard word features. The topic features can also improve the accuracy of the standard word features for the three-class sentiment analysis. Originality/value The standard textual data representation for sentiment analysis using machine learning is bag of word and its extensions mainly created by natural language processing. This paper applies topics as novel features for the machine learning-based sentiment analysis in Indonesian tweets.

Sentiment Classification-How to Quantify Public Emotions Using Twitter

2018 ◽  
Vol 10 (1) ◽  
pp. 57-71 ◽  
Author(s):  
Prerna Mahajan ◽  
Anamika Rana
Keyword(s):  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Research Area ◽  
Sentiment Classification ◽  
Government Policies ◽  
Emotion Detection ◽  
Textual Data ◽  
Good Classification ◽  
Important Research Area ◽  
Sentiment Score

This article describes how with the tremendous popularity in the usage of social media has led to the explosive growth in unstructured data available on various social networking sites. Sentiment analysis of textual data collected from such platforms has become an important research area. In this article, the sentiment classification approach which employs an emotion detection technique is presented. To identify the emotions this paper uses the NRC lexicon based approach for identifying polarity of emotions. A score is computed to quantify emotions obtained from NRC lexicon approach. The method proposed has been tested on twitter datasets of government policies and reforms, more about current NDA government initiatives in India. The polarity components apply and classify the tweets into eight predefined emotions. This article performs both quantitative and sentiment analysis processes with the objective of analyzing the opinion conveyed to each social content, assign a category (+ve, -ve & neutral) or numbered sentiment score. The assigned scores have been classified using six different machine classification algorithms. Good classification results are achieved with the data.

scholarly journals A Multilabel Classifier for Text Classification and Enhanced BERT System

2021 ◽  
Vol 35 (2) ◽  
pp. 167-176
Author(s):  
Bhavana R. Bhamare ◽  
Jeyanthi Prabhu
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Language Model ◽  
Classification Problem ◽  
Automated System ◽  
Two Phase ◽  
Hybrid Features ◽  
Textual Data ◽  
Dependency Rule ◽  
Weighted Correlation

Now-a-day, a vast variety of reviews are published on the web. As a result, an automated system to analyze and extract knowledge from such textual data is needed. Sentiment analysis is a well-known sub-area in Natural Language Processing (NLP). In earlier research, sentiments were determined without considering the aspects specified in a review instance. Aspect-based sentiment analysis (ABSA) has caught the attention of researchers. Many existing systems consider ABSA as a single label classification problem. This drawback is handled in this study by proposing three approaches that use multilabel classifiers for classification. In the first approach, the performance of a model with hybrid features is analyzed using the multilabel classifier. The hybrid feature set includes word dependency rule-based features and unigram features selected using the proposed two-phase weighted correlation feature selection (WCFS) approach. In the second and third approaches Bidirectional Encoder Representation from Transformers (BERT) language model is used. In the second approach, a BERT system is enhanced by applying max pooling on target terms which specify an aspect of a review instance and a multibit label is given as input to the BERT system. In the third approach, the basic BERT system is used for word embedding only and classification is done using multilabel classifiers. In all approaches, the label used for all training instances specifies aspects with its sentiments. The experimentation shows that the results gained using the system proposed in the first approach are comparable to the results gained using the BERT system. The experimental results depict that the Enhanced BERT system gives better results compared to the existing systems.

Enhancements in Decision Making Through Sentiment Analysis of Twitter Data

2020 ◽  
Vol 17 (9) ◽  
pp. 4083-4091
Author(s):  
Jagadish S. Kallimani ◽  
S. H. Ajeya ◽  
D. Keerthana ◽  
Manoj J. Shet ◽  
Prasada Hegde
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Social Networking Sites ◽  
Multi Layer Perceptron ◽  
The Public ◽  
Twitter Data ◽  
Product Service ◽  
Reliable Source ◽  
Computational Identification

All trades and business run predominantly on customer satisfaction and serves as the key to success. Usually, the decisions made by people is largely dependent on others’ perspectives. Hence, it becomes important to have reviews in your favor to sustain and outperform competitors in the market. Collecting reviews and predictions and analyzing them is an effective method to get insights on how the product, service or subject is accepted by the public. It also helps us discover the fields or aspects that needs to be improved. This comes under the field of Sentiment Analysis which refers to the computational identification of views, perspectives, opinions and emotions from text and speech through Natural Language Processing. With the emergence of the internet, blogging and social-networking sites are a rage. Twitter is one of the popular and ubiquitous sites and acts as a reliable source of feedback. In this paper, we seek to detect the emotion portrayed in a given tweet with significant accuracy. We propose the use of Word2Vec model and Count Vectorizer to extract features from pre-processed data. The output is fed to trained Multi-Layer Perceptron classifier to detect the emotion behind the sentence.

scholarly journals Sentiment Analysis of Movie Review using Machine Learning Approach

IJOSTHE ◽  
2017 ◽  
Vol 5 (1) ◽  
pp. 10
Author(s):  
Rajul Rai ◽  
Pradeep Mewada
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
The People ◽  
Textual Data ◽  
Machine Learning Approach ◽  
Regional Languages

With development of Internet and Natural Language processing, use of regional languages is also grown for communication. Sentiment analysis is natural language processing task that extracts useful information from various data forms such as reviews and categorize them on basis of polarity. One of the sub-domain of opinion mining is sentiment analysis which is basically focused on the extraction of emotions and opinions of the people towards a particular topic from textual data. In this paper, sentiment analysis is performed on IMDB movie review database. We examine the sentiment expression to classify the polarity of the movie review on a scale of negative to positive and perform feature extraction and ranking and use these features to train our multilevel classifier to classify the movie review into its correct label. In this paper classification of movie reviews into positive and negative classes with the help of machine learning. Proposed approach using classification techniques has the best accuracy of about 99%.

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