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 of Review Data of a Product Using Python

2018 ◽  
Vol 7 (3.12) ◽  
pp. 674
Author(s):  
P Santhi Priya ◽  
T Venkateswara Rao
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
The Other ◽  
Product Reviews ◽  
Prime Problem

The other name of sentiment analysis is the opinion mining. It’s one of the primary objectives in a Natural Language Processing(NLP). Opinion mining is having a lot of audience lately. In our research we have taken up a prime problem of opinion mining which is theSentiment Polarity Categorization(SPC) that is very influential. We proposed a methodology for the SPC with explanations to the minute level. Apart from theories computations are made on both review standard and sentence standard categorization with benefitting outcomes. Also, the data that is represented here is from the product reviews given on the shopping site called Amazon.  

scholarly journals Application of Naïve Bayes Algorithm in Sentiment Analysis of Filipino, English and Taglish Facebook Comments

2020 ◽  
Vol 4 (5) ◽  
pp. 73-77
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Product Reviews ◽  
Documentary Data ◽  
The Social ◽  
Bayes Algorithm

The World Wide Web has boosted its content for the past years, it has a vast amount of multimedia resources that continuously grow specifically in documentary data. One of the major contributors of documentary contents can be evidently found on the social media called Facebook. People or netizens on Facebook are actively sharing their opinion about a certain topic or posts that can be related to them or not. With the huge amount of accessible documentary data that are seen on the so-called social media, there are research trends that can be made by the researchers in the field of opinion mining. A netizen’s comment on a particular post can either be a negative or a positive one. This study will discuss the opinion or comment of a netizen whether it is positive or negative or how she/he feels about a specific topic posted on Facebook; this is can be measured by the use of Sentiment Analysis. The combination of the Natural Language Processing and the analytics in textual form is also known as Sentiment Analysis that is use to the extraction of data in a useful manner. This study will be based on the product reviews of Filipinos in Filipino, English and Taglish (mixed Filipino and English) languages. To categorize a comment effectively, the Naïve Bayes Algorithm was implemented to the developed web system.

scholarly journals An Exploration of Impact of COVID 19 on mental health -Analysis of tweets using Natural Language Processing techniques

2020 ◽  
Author(s):  
Sohini Sengupta ◽  
Sareeta Mugde ◽  
Garima Sharma
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Gold Mine ◽  
Social Media Platforms ◽  
Processing Techniques

Twitter is one of the world's biggest social media platforms for hosting abundant number of user-generated posts. It is considered as a gold mine of data. Majority of the tweets are public and thereby pullable unlike other social media platforms. In this paper we are analyzing the topics related to mental health that are recently (June, 2020) been discussed on Twitter. Also amidst the on-going pandemic, we are going to find out if covid-19 emerges as one of the factors impacting mental health. Further we are going to do an overall sentiment analysis to better understand the emotions of users.

Coronavirus Pandemic (COVID-19)

2021 ◽  
Vol 17 (2) ◽  
pp. 1-21
Author(s):  
Jalal S. Alowibdi ◽  
Abdulrahman A. Alshdadi ◽  
Ali Daud ◽  
Mohamed M. Dessouky ◽  
Essa Ali Alhazmi
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Experimental Results ◽  
Mixed Emotions ◽  
Social Media Platforms

People are afraid about COVID-19 and are actively talking about it on social media platforms such as Twitter. People are showing their emotions openly in their tweets on Twitter. It's very important to perform sentiment analysis on these tweets for finding COVID-19's impact on people's lives. Natural language processing, textual processing, computational linguists, and biometrics are applied to perform sentiment analysis to identify and extract the emotions. In this work, sentiment analysis is carried out on a large Twitter dataset of English tweets. Ten emotional themes are investigated. Experimental results show that COVID-19 has spread fear/anxiety, gratitude, happiness and hope, and other mixed emotions among people for different reasons. Specifically, it is observed that positive news from top officials like Trump of chloroquine as cure to COVID-19 has suddenly lowered fear in sentiment, and happiness, gratitude, and hope started to rise. But, once FDA said, chloroquine is not effective cure, fear again started to rise.

scholarly journals Opinion Mining Analysis of Twitter Users of Particular Topic

2020 ◽  
pp. 102-108
Author(s):  
Sneha Naik ◽  
Mona Mulchandani
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Stock Exchange ◽  
Daily Basis ◽  
The Public ◽  
Public Sentiment ◽  
Twitter Users

Opinion mining consists of many different fields like natural language processing, text mining, decision making and linguistics. Opinion mining is a type of natural language processing for tracking the mood of the public about a particular product. Opinion mining, which is also called sentiment analysis, involves building a system to collect and categorize opinions about a product. Automated opinion mining often uses machine learning, a type of artificial intelligence (AI), to mine text for sentiment. This project addresses the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in them: positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange.

scholarly journals Sentiment Analysis for Depression Based on Social Media Post by Using Natural Language Processing

2021 ◽  
pp. 134-139
Author(s):  
G. Neelavathi ◽  
D. Sowmiya ◽  
C. Sharmila ◽  
J. Vaishnavi
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Rich Source ◽  
Research Center ◽  
Depression Symptoms ◽  
Linguistic Markers

Presently Research Center expresses that, 72% of public uses some sort of social media. More than 300 million individual experiences the depression and despondency, just a small amount of them get sufficient treatment. Discouragement is the main source of incapacity worldwide and almost 800,000 individuals consistently loss their life because of suicide. Suicide is the subsequent driving reason for death among teenagers. Our idea is to suggest solution for this problem. Social Media gives an extraordinary chance to change early depressions, especially in youngsters. Consistently, around 6,000 Tweets are tweeted per second, 350,000 tweets per minute, 500 million tweets each day and around 200 billion tweets each year. By using this rich source of data and information, can efficient model which provides report of person’s depression symptoms will be designed. In this model an algorithm that can examine Tweets Expressing self-assessed negative features by analyzing linguistic markers in social media posts.

scholarly journals An Innovative Recommender System for E-Commerce Websites using Natural Language Processing

2020 ◽  
Vol 8 (6) ◽  
pp. 4085-4089
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Recommender Systems ◽  
Language Processing ◽  
Recommender System ◽  
Opinion Mining ◽  
The Internet

A Recommender System has become the go-to application for the internet generation these days. Mono-variate, bi-variate and multi-variate Recommender Systems are available to consumers of various products and services for the last 10 years or so only. In this paper, opinion mining dependent sentiment analysis using NLP tools will be used to recommend products to their purchasers on e-commerce websites. The application can be developed on the Python platform can be commercially used and will be precisely used to people who have to spend money without traditionally touching or feeling the item

scholarly journals Sentiment Analysis Using Machine Learning Approaches (Lexicon based on movie review dataset)

2020 ◽  
Vol 2 (3) ◽  
pp. 145-152
Author(s):  
Ayushi Mitra
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Research Work ◽  
Emotional States ◽  
Learning Approaches ◽  
Data Set

Sentiment analysis or Opinion Mining or Emotion Artificial Intelligence is an on-going field which refers to the use of Natural Language Processing, analysis of text and is utilized to extract quantify and is used to study the emotional states from a given piece of information or text data set. It is an area that continues to be currently in progress in field of text mining. Sentiment analysis is utilized in many corporations for review of products, comments from social media and from a small amount of it is utilized to check whether or not the text is positive, negative or neutral. Throughout this research work we wish to adopt rule- based approaches which defines a set of rules and inputs like Classic Natural Language Processing techniques, stemming, tokenization, a region of speech tagging and parsing of machine learning for sentiment analysis which is going to be implemented by most advanced python language.

Sign in / Sign up

Export Citation Format

Share Document