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 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 Real Time Opinion Mining and Analysis of Twitter Data

2018 ◽  
Vol 7 (3.12) ◽  
pp. 351
Author(s):  
K Senthil Kumar ◽  
Mohammad Musab Trumboo ◽  
Vaibhav . ◽  
Satyajai Ahlawat
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Far East ◽  
Lexical Database ◽  
Training Set ◽  
Twitter Data ◽  
The Far East ◽  
The People ◽  
Single Person ◽  
Mass Information

This era, in which we currently stand, is an era of public opinion and mass information. People from all around the globe are joined together through various information junctions to create a global community, where one thing from the far east reaches to the people of the far west within seconds. Nothing is hidden, everything and anything can be scrutinized to its core and through these global criticisms and mass discussions of gigantic magnitude, we have reached to the pinnacle of correct decisions and better choices. These pseudo social groups and data junctions have bombarded our society so much that they now hold the forelock of our opinions and sentiments, ergo, we reach out to these groups to achieve a better outcome. But, all this enormous data and all these opinions cannot be researched by a single person, hence, comes the need of sentiment analysis. In this paper we’ll try to accomplish this by creating a system that will enable us to fetch tweets from twitter and use those tweets against a lexical database which will create a training set and then compare it with the pre-fetched tweets. Through this we will be able to assign a polarity to all the tweets by means of which we can address them as negative, positive or neutral and this is the very foundation of sentiment analysis, so subtle yet so magnificent.  

Opinion mining and sentiment analysis on a Twitter data stream

2012 ◽  
Author(s):  
Balakrishnan Gokulakrishnan ◽  
Pavalanathan Priyanthan ◽  
Thiruchittampalam Ragavan ◽  
Nadarajah Prasath ◽  
AShehan Perera
Keyword(s):  
Sentiment Analysis ◽  
Data Stream ◽  
Opinion Mining ◽  
Twitter Data

scholarly journals Sentiment Analysis for Twitter Data

2020 ◽  
Vol 9 (7S) ◽  
pp. 98-101
Keyword(s):  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Daily Basis ◽  
Support Vector ◽  
Twitter Data ◽  
Internet Users ◽  
Political Views ◽  
Large Numbers ◽  
Machine Learning Approach

With the advancements in web technology and its growth, there's an incredible volume of information present everywhere on the net for internet users and plenty more data is generated on a daily basis. Internet emerged as place for exchanging ideas, sharing opinions, online learning and political views. Social networking sites such as Facebook, Twitter, are rapidly growing as the users are allowed to post and revel their views on various topics, and can discussion with different groups and communities, or post messages across the world. In the area of sentiment analysis large numbers of researchers are working. The main focus is on twitter data for sentiment analysis, that's helpful to research the info within the tweets,where opinions are heterogeneous, highly unstructured, and are either positive,or negative, or neutral.in many cases. In this paper, we provide a study and comparative analysis of existing techniques used for opinion mining through machine learning approach. Naive Bayes & Support Vector Machine, we provide research on twitter data.

In-Depth Opinion Mining and Sentiment Analysis of Twitter Data

2021 ◽  
pp. 37-51
Author(s):  
Eashan Sharma ◽  
Aryan Gaur ◽  
Shefali Singhal
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Twitter Data

scholarly journals MTVRep: A movie and TV show reputation system based on fine-grained sentiment and semantic analysis

2021 ◽  
Vol 11 (2) ◽  
pp. 1613
Author(s):  
Abdessamad Benlahbib ◽  
El Habib Nfaoui
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Semantic Analysis ◽  
Binary Classification ◽  
Classification Problem ◽  
Reputation System ◽  
Language Models ◽  
Fine Grained ◽  
Tv Shows ◽  
Tv Show

Customer reviews are a valuable source of information from which we can extract very useful data about different online shopping experiences. For trendy items (products, movies, TV shows, hotels, services . . . ), the number of available users and customers’ opinions could easily surpass thousands. Therefore, online reputation systems could aid potential customers in making the right decision (buying, renting, booking . . . ) by automatically mining textual reviews and their ratings. This paper presents MTVRep, a movie and TV show reputation system that incorporates fine-grained opinion mining and semantic analysis to generate and visualize reputation toward movies and TV shows. Differently from previous studies on reputation generation that treat the task of sentiment analysis as a binary classification problem (positive, negative), the proposed system identifies the sentiment strength during the phase of sentiment classification by using fine-grained sentiment analysis to separate movie and TV show reviews into five discrete classes: strongly negative, weakly negative, neutral, weakly positive and strongly positive. Besides, it employs embeddings from language models (ELMo) representations to extract semantic relations between reviews. The contribution of this paper is threefold. First, movie and TV show reviews are separated into five groups based on their sentiment orientation. Second, a custom score is computed for each opinion group. Finally, a numerical reputation value is produced toward the target movie or TV show. The efficacy of the proposed system is illustrated by conducting several experiments on a real-world movie and TV show dataset.

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 Sentiment Severity on Location-Based Social Network (LBSN) Data of Natural disasters

2020 ◽  
Vol 8 (5) ◽  
pp. 4219-4224
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Network ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Twitter Data ◽  
Location Based Social Network ◽  
Negative Sentiment ◽  
Disaster Event ◽  
Extract Information

Social media emerged as one of the key components to reach disaster affected people, as they supplement planning and operational coordination. Sentiment analysis was expended to identify, extract or characterize subjective information, such as opinions, expressed in a tweet. The sentiment expressed is analyzed and is classified as positive or negative sentiment, which is not versatile enough to capture the exact sentiment conveyed by the user. Opinion mining is a machine learning process used to extract information conveyed by the user in the form of text. In this paper, the lexical analysis to sentiment analysis of twitter data is employed. Conventionally, the sentiment is conveyed using the polarity of the data but in this paper, sentiment intensity is employed to convey the sentiments. Performing sentiment analysis on tweets gives us the sentiment intensity conveyed by the user, which in turn is used to calculate the severity of the disaster event specified by the user. Further, it is also used to classify the tweets based on their severity. This paper proposes a methodology to extract relevant sentiment information from Location Based Social Network (LBSN) and suggests a unique scale to classify this information to help disaster management authority.

scholarly journals Feeling the Void: Lack of Support for Isolation and Sleep Difficulties in Pregnant Women during the COVID-19 Pandemic Revealed by Twitter Data Analysis

2021 ◽  
Vol 18 (2) ◽  
pp. 393
Author(s):  
Joey Talbot ◽  
Valérie Charron ◽  
Anne TM Konkle
Keyword(s):  
Pregnant Women ◽  
Sentiment Analysis ◽  
Analysis Tool ◽  
Non Profit ◽  
Twitter Data ◽  
Global Pandemic ◽  
Sleeping Problems ◽  
Twitter Data Analysis ◽  
First Time ◽  
Sleep Difficulties

Pregnant women face many physical and psychological changes during their pregnancy. It is known that stress, caused by many factors and life events such as the COVID-19 pandemic, can negatively impact the health of mothers and offspring. It is the first time social media, such as Twitter, are available and commonly used during a global pandemic; this allows access to a rich set of data. The objective of this study was to characterize the content of an international sample of tweets related to pregnancy and mental health during the first wave of COVID-19, from March to June 2020. Tweets were collected using GetOldTweets3. Sentiment analysis was performed using the VADER sentiment analysis tool, and a thematic analysis was performed. In total, 192 tweets were analyzed: 51 were from individuals, 37 from companies, 56 from non-profit organizations, and 48 from health professionals/researchers. Findings showed discrepancies between individual and non-individual tweets. Women expressed anxiety, depressive symptoms, sleeping problems, and distress related to isolation. Alarmingly, there was a discrepancy between distress expressed by women with isolation and sleep difficulties compared to support offered by non-individuals. Concrete efforts should be made to acknowledge these issues on Twitter while maintaining the current support offered.

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