A Study and Comparison of Sentiment Analysis Techniques Using Demonetization

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Krishna Kumar Mohbey ◽  
Brijesh Bakariya ◽  
Vishakha Kalal
Keyword(s):  
Sentiment Analysis ◽  
Text Analysis ◽  
Analytical Approach ◽  
Document Type ◽  
Analysis Techniques

Sentiment analysis is an analytical approach that is used for text analysis. The aim of sentiment analysis is to determine the opinion and subjectivity of any opinion, review, or tweet. The aim of this chapter is to study and compare some of the techniques used to classify opinions using sentiment analysis. In this chapter, different techniques of sentiment analysis have been discussed with the case study of demonetization in India during 2016. Based on the sentiment analysis, people's opinion can be classified on different polarities such as positive, negative, or neutral. These techniques will be classified on different categories based on size of data, document type, and availability. In addition, this chapter also discusses various applications of sentiment analysis techniques in different domains.

A Survey on Sentiment Analysis Techniques for Twitter

2022 ◽  
pp. 57-90
Author(s):  
Surabhi Verma ◽  
Ankit Kumar Jain
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Analysis Techniques ◽  
Goods And Services ◽  
Text Document ◽  
The Subject ◽  
Over Time

People regularly use social media to express their opinions about a wide variety of topics, goods, and services which make it rich in text mining and sentiment analysis. Sentiment analysis is a form of text analysis determining polarity (positive, negative, or neutral) in text, document, paragraph, or clause. This chapter offers an overview of the subject by examining the proposed algorithms for sentiment analysis on Twitter and briefly explaining them. In addition, the authors also address fields related to monitoring sentiments over time, regional view of views, neutral tweet analysis, sarcasm detection, and various other tasks in this area that have drawn the researchers ' attention to this subject nearby. Within this chapter, all the services used are briefly summarized. The key contribution of this survey is the taxonomy based on the methods suggested and the debate on the theme's recent research developments and related fields.

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.

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.

Learning Algorithms of Sentiment Analysis

2022 ◽  
pp. 176-194
Author(s):  
Suania Acampa ◽  
Ciro Clemente De Falco ◽  
Domenico Trezza
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Scientific Community ◽  
Learning Algorithms ◽  
Learning Approaches ◽  
Italian Language ◽  
Analysis Techniques ◽  
Stratified Sample

The uncritical application of automatic analysis techniques can be insidious. For this reason, the scientific community is very interested in the supervised approach. Can this be enough? This chapter aims to these issues by comparing three machine learning approaches to measuring the sentiment. The case study is the analysis of the sentiment expressed by the Italians on Twitter during the first post-lockdown day. To start the supervised model, it has been necessary to build a stratified sample of tweets by daily and classifying them manually. The model to be test provides for further analysis at the end of the process useful for comparing the three models: index will be built on the tweets processed with the aim of detecting the goodness of the results produced. The comparison of the three algorithms helps the authors to understand not only which is the best approach for the Italian language but tries to understand which strategy is to verify the quality of the data obtained.

An Integration of Sentiment Analysis and MCDM Approach for Smartphone Recommendation

2020 ◽  
Vol 19 (04) ◽  
pp. 1037-1063
Author(s):  
Gaurav Kumar ◽  
N. Parimala
Keyword(s):  
Sentiment Analysis ◽  
Decision Support Tool ◽  
Decision Making Process ◽  
Support Tool ◽  
Customer Reviews ◽  
Analysis Techniques ◽  
Online Platforms ◽  
Almost All ◽  
Product Ratings

Today, smartphones are being used to manage almost all aspects of our lives, ranging from personal to professional. Different users have different requirements and preferences while selecting a smartphone. There is ‘no one-size fits all’ remedy when it comes to smartphones. Additionally, the availability of a wide variety of smartphones in the market makes it difficult for the user to select the best one. The use of only product ratings to choose the best smartphone is not sufficient because the interpretation of such ratings can be quite vague and ambiguous. In this paper, reviews of products are incorporated into the decision-making process in order to select the best product for a recommendation. The top five different brands of smartphones are considered for a case study. The proposed system, then, analyses the customer reviews of these smartphones from two online platforms, Flipkart and Amazon, using sentiment analysis techniques. Next, it uses a hybrid MCDM approach, where characteristics of AHP and TOPSIS methods are combined to evaluate the best smartphones from a list of five alternatives and recommend the best product. The result shows that brand1 smartphone is considered to be the best smartphone among five smartphones based on four important decision criteria. The result of the proposed system is also validated by manually annotated customer reviews of the smartphone by experts. It shows that recommendation of the best product by the proposed system matches the experts’ ranking. Thus, the proposed system can be a useful decision support tool for the best smartphone recommendation.

scholarly journals Business intelligence analytics using sentiment analysis-a survey

2019 ◽  
Vol 9 (1) ◽  
pp. 613
Author(s):  
Prakash P. Rokade ◽  
Aruna Kumari D
Keyword(s):  
Big Data ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Business Intelligence ◽  
Text Analysis ◽  
Analysis Techniques ◽  
The People ◽  
Suitable Technique ◽  
Extract Information

Sentiment analysis (SA) is the study and analysis of sentiments, appraisals and impressions by people about entities, person, happening, topics and services. SA uses text analysis techniques and natural language processing methods to locate and extract information from big data. As most of the people are networked themselves through social websites, they use to express their sentiments through these websites.These sentiments are proved fruitful to an individual, business, government for making decisions. The impressions posted on different available sources are being used by organization to know the market mood about the services they are providing. Analyzing huge moods expressed with different features, style have raised challenge for users. This paper focuses on understanding the fundamentals of sentiment analysis, the techniques used for sentiment extraction and analysis. These techniques are then compared for accuracy, advantages and limitations. Based on the accuracy for expexted approach, we may use the suitable technique.

scholarly journals An Effective BERT-Based Pipeline for Twitter Sentiment Analysis: A Case Study in Italian

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 133
Author(s):  
Marco Pota ◽  
Mirko Ventura ◽  
Rosario Catelli ◽  
Massimo Esposito
Keyword(s):  
Sentiment Analysis ◽  
Language Model ◽  
Language Models ◽  
Plain Text ◽  
Analysis Techniques ◽  
Academic Communities ◽  
Text Corpora ◽  
General Basis ◽  
Model Training

Over the last decade industrial and academic communities have increased their focus on sentiment analysis techniques, especially applied to tweets. State-of-the-art results have been recently achieved using language models trained from scratch on corpora made up exclusively of tweets, in order to better handle the Twitter jargon. This work aims to introduce a different approach for Twitter sentiment analysis based on two steps. Firstly, the tweet jargon, including emojis and emoticons, is transformed into plain text, exploiting procedures that are language-independent or easily applicable to different languages. Secondly, the resulting tweets are classified using the language model BERT, but pre-trained on plain text, instead of tweets, for two reasons: (1) pre-trained models on plain text are easily available in many languages, avoiding resource- and time-consuming model training directly on tweets from scratch; (2) available plain text corpora are larger than tweet-only ones, therefore allowing better performance. A case study describing the application of the approach to Italian is presented, with a comparison with other Italian existing solutions. The results obtained show the effectiveness of the approach and indicate that, thanks to its general basis from a methodological perspective, it can also be promising for other languages.

scholarly journals Evaluation of Sentiment Data using Classifier Model in Rapid Miner Tool

2019 ◽  
Vol 9 (1) ◽  
pp. 2966-2972
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Text Analysis ◽  
Group Communication ◽  
Analysis Techniques ◽  
Online Portal ◽  
Almost All ◽  
Extract Information

Evaluation of internet and the usage of internet as websites which is for penetrating to gain a specific requirements, like group communication as social networks (such as face book, twitter,etc.,) ,blogs for opinions, online portals (such as iGoogle, MSN) for communication, experience as reviews, suggestions as opinions, combination of reviews and opinions as recommendations, ratings and feedbacks which is identified and elevating in almost all the field now-a-days. The writers of online portal, review, opinion and recommendation in any social media take measures as beneficial factor for the improvement of businesses, organization, governments and mostly individuals. When this content boost up the study of content and the need of data mining, text mining techniques and sentiment analysis is inescapable. Natural language processing and text analysis techniques are used in sentiment analysis to recognize and extract information from the text [1]. This paper provides a result of sentiment analysis with the intellectual tool named Rapid Miner to show the sentiment comments about the contents in the online traders.

Failure Analysis of CDM-ESD Damage in a GaAs RFIC

2000 ◽  
Author(s):  
Amy Poe ◽  
Steve Brockett ◽  
Tony Rubalcava
Keyword(s):  
Radio Frequency ◽  
Failure Analysis ◽  
Integrated Circuit ◽  
Production Test ◽  
Analysis Techniques ◽  
Device Model ◽  
Charged Device ◽  
Subsequent Failure

Abstract The intent of this work is to demonstrate the importance of charged device model (CDM) ESD testing and characterization by presenting a case study of a situation in which CDM testing proved invaluable in establishing the reliability of a GaAs radio frequency integrated circuit (RFIC). The problem originated when a sample of passing devices was retested to the final production test. Nine of the 200 sampled devices failed the retest, thus placing the reliability of all of the devices in question. The subsequent failure analysis indicated that the devices failed due to a short on one of two capacitors, bringing into question the reliability of the dielectric. Previous ESD characterization of the part had shown that a certain resistor was likely to fail at thresholds well below the level at which any capacitors were damaged. This paper will discuss the failure analysis techniques which were used and the testing performed to verify the failures were actually due to ESD, and not caused by weak capacitors.

Advanced IR-OBIRCH Analysis Technique for High Isb Failure Analysis

2010 ◽  
Author(s):  
Kuo Hsiung Chen ◽  
Wen Sheng Wu ◽  
Yu Hsiang Shu ◽  
Jian Chan Lin
Keyword(s):  
Failure Analysis ◽  
Failure Mechanisms ◽  
Fault Localization ◽  
Resistance Change ◽  
The Real ◽  
Analysis Techniques ◽  
Analysis Technique ◽  
Leakage Failure ◽  
Failure Site

Abstract IR-OBIRCH (Infrared Ray – Optical Beam Induced Resistance Change) is one of the main failure analysis techniques [1] [2] [3] [4]. It is a useful tool to do fault localization on leakage failure cases such as poor Via or contact connection, FEoL or BEoL pattern bridge, and etc. But the real failure sites associated with the above failure mechanisms are not always found at the OBIRCH spot locations. Sometimes the real failure site is far away from the OBIRCH spot and it will result in inconclusive PFA Analysis. Finding the real failure site is what matters the most for fault localization detection. In this paper, we will introduce one case using deep sub-micron process generation which suffers serious high Isb current at wafer donut region. In this case study a BEoL Via poor connection is found far away from the OBIRCH spots. This implies that layout tracing skill and relation investigation among OBIRCH spots are needed for successful failure analysis.

Sign in / Sign up

Export Citation Format

Share Document