scholarly journals An Overview of Tools and Technologies Used for Opinion Mining and Sentiment Analysis

2021 ◽  
pp. 38-41
Author(s):  
Dr. A. Komathi ◽  
P. Nithya
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Vital Role ◽  
Unstructured Data ◽  
Analysis Tool ◽  
Challenging Problem ◽  
Huge Amount ◽  
Product Sale ◽  
Product Service ◽  
Consumer's Perception

The endeavor of social media has formed many chances for people to publicly voice their beliefs, simply when they are employed to deliver an opinion hit a vital problem. Sentiment analysis is the process to finding the satisfaction information of a consumer’s perception about product, service or brand. Sentiment analysis is also called as opinion mining because it dealt with the huge amount of customer opinion. The analyzing process of customer opinion is playing a vital role in product sale. Sentiment analysis is to extract the features by the notions from others perception about particular product and buying experience. The Sentiment Analysis tool is to function on a series of expressions for a given item based on the quality and features.. To find the opinion rate in the form of unstructured data is been a challenging problem today. Thus, this paper discusses about Sentiment analysis methods and tools which are used to make clear opinion mining.

scholarly journals A Survey on Analysis of Twitter Opinion Mining Using Sentiment Analysis

2019 ◽  
pp. 537-542
Author(s):  
Vishnu VardanReddy ◽  
Mahesh Maila ◽  
Sai Sri Raghava ◽  
Yashwanth Avvaru ◽  
Sri. V. Koteswarao
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Online Communication ◽  
Valuable Insight ◽  
Huge Amount ◽  
Twitter Data ◽  
Product Service ◽  
Different Types

In recent years, there is a rapid growth in online communication. There are many social networking sites and related mobile applications, and some more are still emerging. Huge amount of data is generated by these sites everyday and this data can be used as a source for various analysis purposes. Twitter is one of the most popular networking sites with millions of users. There are users with different views and varieties of reviews in the form of tweets are generated by them. Nowadays Opinion Mining has become an emerging topic of research due to lot of opinionated data available on Blogs & social networking sites. Tracking different types of opinions & summarizing them can provide valuable insight to different types of opinions to users who use Social networking sites to get reviews about any product, service or any topic. Analysis of opinions & its classification on the basis of polarity (positive, negative, neutral) is a challenging task. Lot of work has been done on sentiment analysis of twitter data and lot needs to be done. In this paper we discuss the levels, approaches of sentiment analysis, sentiment analysis of twitter data, existing tools available for sentiment analysis and the steps involved for same. Two approaches are discussed with an example which works on machine learning and lexicon based respectively.

scholarly journals IMPACT OF THE VISUAL DESIGN LANGUAGE OF SOCIAL MEDIA ADVERTISEMENTS ON CONSUMER PERCEPTIONS

2021 ◽  
Vol 9 (1) ◽  
pp. 810-817
Author(s):  
Vishal Kaushik
Keyword(s):  
Design Theory ◽  
Vital Role ◽  
Visual Design ◽  
Sample Group ◽  
The People ◽  
Product Service ◽  
Principles Of Design ◽  
Level Of Understanding ◽  
Visual Properties ◽  
Consumer's Perception

There are plenty of ways to promote or sell a product, service, idea or an event, and only recently, there has been a surge in research that proves that visual aesthetics and design play a vital role in making an advertisement appealing or attractive to the consumer to buy or use. The key purpose of this paper is to establish the principles or a framework that an advertiser keeps in mind while trying to appeal to the consumer’s perception of a brand offering. It shows how important visual design and imageries are in impacting the minds of the consumer. The people in the market are smart and they are able to identify the elements and traits in a design that makes it more appealing to them. And therefore, if a product’s communication appeals to its consumers, they are bound to purchase the said offering. Hence this study becomes very important for the brands, advertisers and the marketing industry as it tells them what makes their communications more attractive to their customers. Thus, the paper investigates how a customer’s opinion impacts their perception about the visual designs presented in the study. The study allows the people of the sample group to pick their favoured design out of the options given based on a particular principal of design. The sample group is henceforth tested on various other principles of design theory and hence we establish the level of understanding the group possesses towards design and aesthetics of any product.      The findings from empirical analysis indicates that visual designs of various ads tend to play a vital role when it comes to swaying the perception of consumers. In was particularly observed in research that perceptive “matching” of target segment needs (desiring product being presented in a visually pleasing manner) and visual properties utilized in adverts (verbal or visual predominately) seems to be vital. The utilization of images/visual design has an impact on the consumer’s perception as it is able to uplift recall value, motivate their attitude toward company’s promoted objects, and therefore harness their psychological intentions. Visual communication grabs attention as adverts have become so meta and complex in its efforts to use design & aesthetics to persuade customers and seize their focus.

Detecting similar opinion holders for massive sentiment analysis

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Erdem Alparslan ◽  
Adem Karahoca
Keyword(s):  
Sentiment Analysis ◽  
Real Time ◽  
Data Analytics ◽  
Opinion Mining ◽  
Big Data Analytics ◽  
Unstructured Data ◽  
Map Reduce ◽  
Time Calculation ◽  
Hadoop Clusters ◽  
Accuracy Rates

Sentiment Analysis is the study of acquisition, extraction and interpretation of human opinions, sentiments, attitudes and emotions from both structured and unstructured data sources. Also called opinion mining, the field is becoming crucial for various application areas including market researches, politics, sociology and economics. Therefore, many outstanding research efforts are performed on the fields including both theoretical and practical aspects. This paper aims to develop a supportive framework for sentiment analysis, focusing on the similarity of opinion holders in a massive dataset. We used e-commerce review dataset of Amazon spanning May 1996 – July 2014. The whole review set includes more than 140 million entries. As a preprocessing task each review is structured and expressed on a quadruple form of 4 dimensions: Target entity, opinion holder, sentiment and time. The aim of this study is to find out similar opinion holders for a given customer on a certain product in real time. We have defined a new method spanning all the opinions of an individual. The idea behind this calculation of similarity is rating of the same product with the same sentiment factor by two different opinion holders. The real-time calculation is also performed on Hadoop clusters.  Performance enhancements and accuracy rates are then discussed.Keywords: sentiment analysis, opinion mining, big data analytics, Map-Reduce

Tasks, Approaches, and Avenues of Opinion Mining, Sentiment Analysis, and Emotion Analysis

2021 ◽  
pp. 171-209
Author(s):  
Amira M. Idrees ◽  
Fatma Gamal Eldin ◽  
Amr Mansour Mohsen ◽  
Hesham Ahmed Hassan
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Potential Benefit ◽  
Opinion Mining ◽  
Product Reviews ◽  
Huge Amount ◽  
Know How

Every successful business aims to know how customers feel about its brands, services, and products. People freely express their views, ideas, sentiments, and opinions on social media for their day-to-day activities, for product reviews, for surveys, and even for their public opinions. This process provides a fortune of valuable resources about the market for any type of business. Unfortunately, it's impossible to manually analyze this massive quantity of information. Sentiment analysis (SA) and opinion mining (OM), as new fields of natural language processing, have the potential benefit of analyzing such a huge amount of data. SA or OM is the computational treatment of opinions, sentiments, and subjectivity of text. This chapter introduces the reader to a survey of different text SA and OM proposed techniques and approaches. The authors discuss in detail various approaches to perform a computational treatment for sentiments and opinions with their strengths and drawbacks.

scholarly journals Sentiment Analysis on Myocardial Infarction Using Tweets Data

2019 ◽  
Vol 8 (S1) ◽  
pp. 10-14
Author(s):  
M. B. Monicka ◽  
A. Krishnaveni
Keyword(s):  
Risk Factors ◽  
Myocardial Infarction ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Disease Risk ◽  
Vital Role ◽  
Mining System ◽  
Machine Learning Approach ◽  
Rich Information ◽  
The Rich

In 2016, the survey reports that 1.7 Million people die of Myocardial Infarction (MI), due to less medication facilities, less prevention care and treatment planning is top most analysis of effective disease risk assessment, through this we have take prevention using sentiment analysis of recent advancements, the text analytics have opened up new potential of using the rich information of tweet analysis, to identify the relevant risk factors in MI. To tackle the MI risk factors tweet analysis gives more remedy and care factors by users, also this leads to decrease of MI in India. Our system plays a machine learning approach using sentiment analysis using tweet dataset. Nowadays people suffering from MI such as cardiac arrest, high blood pressure, congestive heart failure etc. Twitter is an excellent resource for the MI Patients since they connect people who have with similar conditions and experiences. It provides the knowledge sharing about MI, plays a vital role through Opinion Mining system.

Tools of Opinion Mining

2019 ◽  
pp. 179-203
Author(s):  
Neha Gupta ◽  
Siddharth Verma
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Semantic Analysis ◽  
Analysis Tool ◽  
Search Results ◽  
Twitter Data

Today's generation express their views and opinions publicly. For any organization or for individuals, this feedback is very crucial to improve their products and services. This huge volume of reviews can be analyzed by opinion mining (also known as semantic analysis). It is an emerging field for researchers that aims to distinguish the emotions expressed within the reviews, classifying them into positive or negative opinions, and summarizing it into a form that is easily understood by users. The idea of opinion mining and sentiment analysis tool is to process a set of search results for a given item based on the quality and features. Research has been conducted to mine opinions in form of document, sentence, and feature level sentiment analysis. This chapter examines how opinion mining is moving to the sentimental reviews of Twitter data, comments used in Facebook on pictures, videos, or Facebook statuses. Thus, this chapter discusses an overview of opinion mining in detail with the techniques and tools.

scholarly journals Recommendation of Distance Education Colleges to Students based on Alumni Feedback

2020 ◽  
Vol 9 (1) ◽  
pp. 2357-2363
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Mining System ◽  
Product Service ◽  
Effective System ◽  
Analysis System ◽  
The Individual ◽  
Implicit And Explicit ◽  
Fake Reviews

Sentiment Analysis (SA) systems are very common because most people trust it based on the opinions, emotions, attitudes and feelings shared by the users for decision making purposes about the product, service, news analytics etc. Sentiment analysis or opinion mining is used to automatically detect and classify sentiments into positive, negative or neutral opinion on product or service through certain algorithms. The expeditious growth of internet leads to the increase of reviews about product, services, movies, restaurants or vacation destinations and organizations. In order to increase or decrease the market value of the product, spammers may give the fake ratings. Sentiment Analysis system face great difficulties in deploying the algorithms to classify each review as either honest review, posted by the customers after using the products, or spam review, posted by the individual spammer or spammer groups. Another major challenge faced by the sentiment analysis system is that it lacks the accuracy of predicting implicit and explicit features present in the dataset is low, which is the major challenge in opinion mining system. The proposed system deals with text pre-processing which helps in improving the overall performance of the sentiment analysis systems and an effective system is developed to identify the fake reviews present in the dataset. Association Rule Mining along with K-Means clustering is used to achieve higher efficiency in classification of implicit and explicit features. Lexicon method is used for the classification of sentiments into positive and negative polarities. The advantage of proposed system is that, it can identify and remove the fake reviews in the dataset and extraction of both implicit and explicit feature can be identified through Lexicon based Method along with its polarities.

Political Opinion Mining from Twitter

2016 ◽  
Vol 8 (4) ◽  
pp. 47-56 ◽  
Author(s):  
Yashvardhan Sharma ◽  
Ekansh Mittal ◽  
Mayank Garg
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Online Media ◽  
Analysis Tool ◽  
Political Leaders ◽  
Political Opinion ◽  
Political Views ◽  
Over Time

Twitter is one of the most popular micro-blogging platform for people to express their political views in and around the elections. Hence during pre-elections twitter becomes a rich resource of data to understand the changing tenor of political leaders with time. During this time, when views, opinions and judgments are shared so prolifically through online media, tools which can provide the crux of this content are paramount. In this paper the authors have developed one such sentiment analysis tool to analyze the changing political views of persons with time. Using the tool they classify the tweets as positive, negative or neutral and studying it over time the authors successfully estimate the mood of the person. The authors have also developed a specialized phonetic dictionary to provide better approximation for most commonly used slangs and abbreviations.

scholarly journals SENTIMENT ANALYSIS OF ENGLISH TWEETS USING BIGRAM COLLOCATION

2021 ◽  
pp. 220-227
Author(s):  
Sumaya Ishrat Moyeen ◽  
Md. Sadiqur Rahman Mabud ◽  
Zannatun Nayem ◽  
Md. Al Mamun
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Maximum Entropy ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Daily Life ◽  
Naïve Bayes ◽  
Support Vector ◽  
Huge Amount

Community and portal websites like Twitter, Facebook, Tumbler, Instagram, and LinkedIn etc. have significant impact in our day-to-day life. One of the most popular micro-blogging platforms is twitter that can provide a huge amount of data which in future can be used for various applications of opinion mining like predictions, reviews, elections, marketing etc. The users use this platform to share their views, express sentiments on various events of their daily life. Previously, many researchers have worked with twitter sentiment analysis and compared various classifiers and got the accuracy below 82%. In this work for classifying tweets into sentiments, we have used various classifiers such as Naïve Bayes, Support Vector Machine and Maximum Entropy that segregate the positive and negative tweets. Using Bigram Collocation with classifiers, we’ve acquired 88.42% accuracy. KEYWORDS: Twitter; Sentiment Classification; Machine Learning; NLTK; Python; Naïve Bayes; Support Vector Machine (SVM); Maximum Entropy

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