Text Analytics: Integrated sentiment analysis

2019 ◽  
Keyword(s):  
Sentiment Analysis ◽  
Text Analytics

scholarly journals The Power of Social Media Analytics: Text Analytics Based on Sentiment Analysis and Word Clouds on R

2018 ◽  
Vol 22 (1/2018) ◽  
pp. 25-38
Author(s):  
Ahmed Imran KABIR ◽  
Ridoan KARIM ◽  
Shah NEWAZ ◽  
Muhammad Istiaque HOSSAIN
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Social Media Analytics ◽  
Text Analytics ◽  
Word Clouds

scholarly journals Sentiment Analysis using Optimized Feature Sets in Different Twitter Dataset Domains

2019 ◽  
Vol 8 (11) ◽  
pp. 3035-3039
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Support Vector ◽  
Text Analytics ◽  
K Nearest Neighbors ◽  
Verbal Language ◽  
Feature Sets ◽  
Goods And Services ◽  
Assessment Metrics

in the last years, the relevance of sentiment analysis is broad and dominant. The capability to take out insights from social data is a tradition that is being extensively accepted by all over globe. Sentiment Analysis has turn out to be a hot-trend issue of technical and marketplace research in the area of Natural Language Processing (NLP) and Machine Learning. Sentiment analysis is enormously useful in social media supervising as it permits us to expand an impression of the wider open estimation behind definite topics. Investigation of social media streams is typically limited to just essential sentiment analysis and count based metrics. This is of the same kind to just scratching the outside and missing out on those elevated value insight that is ahead of you to be discovered. There’s a lot of effort to be done, but perfections are being prepared every day. It is a way to appraise on paper or verbal language to settle on if the expression is favorable, unfavorable, or unbiased, and to what level. Today’s algorithm-based sentiment analysis tools can touch vast amount of client response constantly and precisely. Balancing with text analytics, sentiment analysis exposes the customer’s estimation concerning topics ranging from your goods and services to your position, your advertisements, or even your challengers. These efforts scrutinize the crisis of studying texts, like posts and reviews, uploaded by user on Twitter. The Support Vector Machine (SVM), k-nearest neighbors algorithm (KNN) and proposed optimized feature sets model is offered to progression the tweet features and to recognize the out of sight sentiments from these tweets. These essential concepts when used in combinations become a very significant tool for analyzing millions of variety conversations with human echelon accurateness. The projected optimized feature sets model Sentiment Analysis exercise the assessment metrics of Precision, Recall, F-score, and Accuracy. Also, average measures weighted F1-scores are constructive for categorization of Positive, Negative and Neutral multi-class problems. The running time of the technique is evaluates by accomplishing diverse methods in the same investigational setup consisting a cluster of 8 nodes. Planned optimized feature sets model Sentiment Analysis reachs 82 % accuracy as compare with SVM 78.6 % and KNN 75 %. Further, while analyzing sentiments of tweets we have measured only tweets in English acknowledged by Twitter streaming API.

scholarly journals Impact of News Sentiment Analysis on Market Price Movement During the Dividend Period

2018 ◽  
Vol 7 (4.11) ◽  
pp. 168
Author(s):  
W. L. Hor ◽  
W. X. Goh ◽  
S. H. Ow
Keyword(s):  
Stock Market ◽  
Sentiment Analysis ◽  
Stock Prices ◽  
Market Price ◽  
Reliable Data ◽  
News Daily ◽  
Text Analytics ◽  
Normal Range ◽  
Price Movement ◽  
News Sentiment

This study presents development of a system for analysing the polarity of stock market news to guide traders in making better decision when buying, selling or holding stocks during the dividend period. It will also help traders by reducing the risk of making inaccurate decision in trading. Trusted and reliable data such as dividend news, daily share market price, company news and announcements from Bursa Malaysia and The Edge Market will be used for performing news sentiment analysis using Azure Text Analytics. The results show that company news and announcements do not have significant effect on the Malaysia stock prices as the prices move within the range of 0-1%, which is the benchmark of the normal range of daily price movement.  

scholarly journals Sentiment Analysis for Fake News Detection

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1348
Author(s):  
Miguel A. Alonso ◽  
David Vilares ◽  
Carlos Gómez-Rodríguez ◽  
Jesús Vilares
Keyword(s):  
Sentiment Analysis ◽  
Well Being ◽  
Political Polarization ◽  
Text Analytics ◽  
Fake News ◽  
Huge Amount ◽  
Design And Implementation ◽  
Near Future ◽  
Manual Verification ◽  
Automatic Systems

In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements.

scholarly journals Text Mining and Sentiment Analysis of Newspaper Headlines

Information ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 414
Author(s):  
Arafat Hossain ◽  
Md. Karimuzzaman ◽  
Md. Moyazzem Hossain ◽  
Azizur Rahman
Keyword(s):  
Text Mining ◽  
Sentiment Analysis ◽  
Front Page ◽  
Text Analytics ◽  
Law And Order ◽  
Efficient Manner ◽  
Positive Type ◽  
Word Clouds ◽  
And Cluster Analysis ◽  
Political Turmoil

Text analytics are well-known in the modern era for extracting information and patterns from text. However, no study has attempted to illustrate the pattern and priorities of newspaper headlines in Bangladesh using a combination of text analytics techniques. The purpose of this paper is to examine the pattern of words that appeared on the front page of a well-known daily English newspaper in Bangladesh, The Daily Star, in 2018 and 2019. The elucidation of that era’s possible social and political context was also attempted using word patterns. The study employs three widely used and contemporary text mining techniques: word clouds, sentiment analysis, and cluster analysis. The word cloud reveals that election, kill, cricket, and Rohingya-related terms appeared more than 60 times in 2018, whereas BNP, poll, kill, AL, and Khaleda appeared more than 80 times in 2019. These indicated the country’s passion for cricket, political turmoil, and Rohingya-related issues. Furthermore, sentiment analysis reveals that words of fear and negative emotions appeared more than 600 times, whereas anger, anticipation, sadness, trust, and positive-type emotions came up more than 400 times in both years. Finally, the clustering method demonstrates that election, politics, deaths, digital security act, Rohingya, and cricket-related words exhibit similarity and belong to a similar group in 2019, whereas rape, deaths, road, and fire-related words clustered in 2018 alongside a similar-appearing group. In general, this analysis demonstrates how vividly the text mining approach depicts Bangladesh’s social, political, and law-and-order situation, particularly during election season and the country’s cricket craze, and also validates the significance of the text mining approach to understanding the overall view of a country during a particular time in an efficient manner.

যন্ত্র-না (Jantra-Na Not-Machine) Can Only Feel যন্ত্রনা (Jantrana Pain)!

2013 ◽  
pp. 328-345
Author(s):  
Amitava Das ◽  
Björn Gambäck
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Computational Linguistics ◽  
Language Processing ◽  
Opinion Mining ◽  
Text Analytics ◽  
Emotional Information ◽  
Solution Strategies ◽  
Basic Task

Arguably, the most important difference between machines and humans is that humans have feelings. For several decades researchers have been trying to create methods to simulate sentimentality for machines, and currently Sentiment Analysis is the hottest, most demanding, and rapidly growing task in the language processing field. Sentiment analysis or opinion mining refers to the application of Natural Language Processing, Computational Linguistics, and text analytics to identify and extract sentimental (opinionated, emotional) information in a text. The basic task in sentiment analysis is to classify the polarity of a given text at the document, sentence, or feature/aspect level, that is, to decide whether the expressed sentiment in a document, a sentence, or a feature/aspect is positive (happy), negative (sad), neutral (memorable), and so forth. In this chapter, the authors discuss various challenges and solution strategies for Sentiment Analysis with a particular view to texts in Bangla (Bengali).

scholarly journals Sentiment Analysis of Food Recipe Comments

1970 ◽  
Vol 9 (2) ◽  
pp. 182-193
Author(s):  
Pakawan Pugsee ◽  
Monsinee Niyomvanich
Keyword(s):  
Sentiment Analysis ◽  
Text Analytics ◽  
Negative Comment ◽  
User Comments ◽  
Negative Comments ◽  
Positive Comment

Sentiment analysis of food recipe comments is to identify user comments about the food recipes to the positive or the negative comments. The proposed method is suitable for analysing comments or opinions about food recipes by counting the polarity words on the food domain. The benefit of this research is to help users to choose the preferred recipes from different food recipes on online food communities. To analyse food recipes, the comments of each recipe from members of the community will be collected and classified to neutral, positive or negative comments. All recipes’ comment messages are processed using text analytics and the generated polarity lexicon. Therefore, the user can gain the information to make a smart decision. The evaluation of the comment analysis shows that the accuracy of neutral and positive comment classification is about 90%. In addition, the accuracy of negative comment identification is more than 70%.

Study of Sentiment of Governor's Election Opinion in 2018

2018 ◽  
pp. 231-238
Author(s):  
Agung Eddy Suryo Saputro ◽  
Khairil Anwar Notodiputro ◽  
Indahwati A
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Addition Method ◽  
Sentiment Mining ◽  
Positive Sentiment ◽  
Trending Topics ◽  
Election News

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

Sign in / Sign up

Export Citation Format

Share Document