scholarly journals Language Identification for Multilingual Sentiment Examination

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3571-3576
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
English Language ◽  
Language Identification ◽  
Parts Of Speech ◽  
Analysis Task ◽  
E Learning ◽  
Media Platform ◽  
Speech Tagging ◽  
Text Sentiment Analysis

Social media is most popular platform on which users can share their views, reviews and knowledge about various topics, news, products etc. Identifying sentiments or opinions of users is valuable for many e-commerce companies, Hotels, e-learning etc. This opinion analysis is useful for companies to improve their service and products. Due to increase in web users across globe, users happen to post their views freely over the internet. Many different languages are spoken across globe, supporting multilingual nature of social media makes analysis of such text difficult. Sentiment analysis can be conducted using videos, image, text, where text sentiment analysis is most popular form because of freely available contents in the form of blogs, reviews, comments etc. Because of development of social media platform, people can post comment in any language, creates the need for Multilingual sentiment analysis. Sentiment analysis task needs phases such as data collection, pre-processing, sentiment classification and polarity identification. The Multilingual nature needs Script Identification on the input text by labelling the different words used in text along with scripts used to denote them. Various languages used in the text are identified and the Hindi language text written in Romanized script is transliterated to Devanagari script. Text is then completely translated into English language and POS(Parts of Speech) tagging is performed on the obtained text. The aim and purpose of this study is to survey different techniques of multilingual sentiment analysis, and language identification of source text, where n-grams model outperforms all.

scholarly journals A Check on Annotation in Sentiment Research

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1346-1350
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
English Language ◽  
Research Area ◽  
Research Literature ◽  
Manual Annotation ◽  
Automatic Annotation ◽  
Annotation Method ◽  
New Concepts

The research literature on sentiment analysis methodologies has exponentially grown in recent years. In any research area, where new concepts and techniques are constantly introduced, it is, therefore, of interest to analyze the latest trends in this literature. In particular, we have chosen to primarily focus on the literature of the last five years, on annotation methodologies, including frequently used datasets and from which they were obtained. Based on the survey, it appears that researchers do more manual annotation in the formation of sentiment corpus. As for the dataset, there are still many uses of English language taken from social media such as Twitter. In this area of research, there are still many that need to be explored, such as the use of semi-automatic annotation method that is still very rarely used by researchers. Also, less popular languages, such as Malay, Korean, Japanese, and so on, still require corpus for sentiment analysis research.

A Sentiment Analysis of the 2014-15 Ebola Outbreak in the Media and Social Media

2020 ◽  
pp. 202-212
Author(s):  
Blooma John ◽  
Bob Baulch ◽  
Nilmini Wickramasinghe
Keyword(s):  
Social Media ◽  
West Africa ◽  
Sentiment Analysis ◽  
Media Coverage ◽  
Social Media Platform ◽  
The Usa ◽  
Media Platform ◽  
The Media ◽  
Precautionary Measures ◽  
Increased Pressure

The negative and unbalanced nature of media and social media coverage has amplified anxieties and fears about the Ebola outbreak. The authors analyse news articles on the Ebola outbreak from two leading news outlets, together with comments on the articles from a well-known social media platform, from March 2014 to July 2015. The volume of news articles was greatest between August 2014 and January 2015, with a spike in October 2014, and was driven by the few cases of transmission in Europe and the USA. Sentiment analysis reveals coverage and commentary on the small number of Ebola cases in Europe and the USA were much more extensive than coverage and commentary on the outbreak in West Africa. Articles expressing negative sentiments were more common in the USA and also received more comments than those expressing positive sentiments. The negative sentiments expressed in the media and social media amplified fears about an Ebola outbreak outside West Africa, which increased pressure for unwarranted and wasteful precautionary measures.

scholarly journals Multi Class Data Classification to Improve Accuracy in Sentiment Analysis using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 1457-1461
Author(s):  
Daram Vishnu
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Confusion Matrix ◽  
Training Data ◽  
Natural Languages ◽  
Parts Of Speech ◽  
Testing Data ◽  
Improve Accuracy ◽  
Textual Form ◽  
Speech Tagging

Sentiment analysis means classifying a text into different emotional classes. These days most of the sentiment analysis techniques divide the text into either binary or ternary classification in this paper we are classifying the movie reviews into 5 classes. Multi class sentiment analysis is a technique which can be used to know the exact sentiment of a review not just polarity of a given textual statement from positive to negative. So that one can know the precise sentiment of a review . Multi class sentiment analysis has always been a challenging task as natural languages are difficult to represent mathematically. The number of features are also generally large which requires huge computational power so to reduce the number of features we will use parts-of-speech tagging using textblob to extract the important features. Sentiment analysis is done using machine learning, where it requires training data and testing data to train a model. Various kinds of models are trained and tested at last one model is selected based on its accuracy and confusion matrix. It is important to analyze the reviews in textual form because large amount of reviews is present all over the web. Analyzing textual reviews can help the firms that are trying to find out the response of their products in the market. In this paper sentiment analysis is demonstrated by analyzing the movie reviews, reviews are taken from IMDB website.

scholarly journals Natural Language to SQL query Generation

2021 ◽  
Vol 9 (VI) ◽  
pp. 5069-5072
Author(s):  
Kiran Raj R
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
English Language ◽  
Regular Expression ◽  
Parts Of Speech ◽  
Query Generation ◽  
Sql Query ◽  
Speech Tagging ◽  
The Web

Today, everyone has a personal device to access the web. Every user tries to access the knowledge that they require through internet. Most of the knowledge is within the sort of a database. A user with limited knowledge of database will have difficulty in accessing the data in the database. Hence, there’s a requirement for a system that permits the users to access the knowledge within the database. The proposed method is to develop a system where the input be a natural language and receive an SQL query which is used to access the database and retrieve the information with ease. Tokenization, parts-of-speech tagging, lemmatization, parsing and mapping are the steps involved in the process. The project proposed would give a view of using of Natural Language Processing (NLP) and mapping the query in accordance with regular expression in English language to SQL.

scholarly journals Sentiment Analysis Of Cyberbullying On Twitter Using SentiStrength

2020 ◽  
Vol 3 (1) ◽  
pp. 21
Author(s):  
Ulfa Khaira ◽  
Ragil Johanda ◽  
Pradita Eko Prasetyo Utomo ◽  
Tri Suratno
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Data Set ◽  
Social Media Platform ◽  
The Past ◽  
Exchange Information ◽  
Media Platform ◽  
Assessment Results

Cyberbullying is a form of bullying that takes place across virtually every social media platform. Twitter is a form of social media that allows users to exchange information. Bullying has been a growing problem on Twitter over the past few years. Sentiment analysis is done to identify the element of bullying in a tweet. Sentiments are divided into 3 classes, namely Bullying, Non-Bullying and neutral. There are three steps to classify cyberbullying i.e. collection of data set, preprocessing data, and classification process. This research used sentiStrength, an algorithm which uses a lexicon based approach. This SentiStrength lexicon contains the weight of its sentiment strength. The assessment results from 454 tweets data obtained 161 tweet non-bullying (35.4%), 87 tweet neutral (19.1%), and 206 tweet bullying (45.4%). This research produces an accuracy value of 60.5%.

scholarly journals Polarity Classification Tool for Sentiment Analysis in Malay Language

2019 ◽  
Vol 8 (3) ◽  
pp. 259
Author(s):  
Normi Sham Awang Abu Bakar ◽  
Ros Aziehan Rahmat ◽  
Umar Faruq Othman
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
English Language ◽  
Sentiment Lexicon ◽  
The Social ◽  
Polarity Classification ◽  
Media Channels ◽  
Classification Tool

<p>The popularity of the social media channels has increased the interest among researchers in the sentiment analysis(SA) area. One aspect of the SA research is the determination of the polarity of the comments in the social media, i.e. positive, negative, and neutral. However, there is a scarcity of Malay sentiment analysis tools because most of the work in the literature discuss the polarity classification tool in English. This paper presents the development of a polarity classification tool called Malay Polarity Classification Tool(MaCT). This tool is developed based on the AFINN sentiment lexicon for English language. We have attempted to translate each word in AFINN to its Malay equivalent and later, use the lexicon to collect the sentiment data from Twitter. The Twitter data are then classified into positive, negative, and neutral. For the validation purpose, we collect 400 positive tweets, 400 negative tweets, and 200 neutral tweets, and later, run the tweets through our sentiment lexicon and found 90% score for precision, recall and accuracy. Our main contribution in the research is the new AFINN translation for Malay language and also the classification of the sentiment data.</p>

scholarly journals Information Sharing through Twitter by Public Health care Institution during COVID-19 Pandemic: A Case Study of AIIMS, Raipur

2021 ◽  
Vol 33 (1) ◽  
pp. 189-192
Author(s):  
Shiv Shankar Sharma ◽  
Daljeet Kaur ◽  
Taranjeet Kaur Chawla ◽  
Vaishali Kapoor
Keyword(s):  
Public Health ◽  
Health Care ◽  
Social Media ◽  
Sentiment Analysis ◽  
Public Health Care ◽  
Institutional Image ◽  
Health Care Institutions ◽  
Media Platform ◽  
Two Phases

Background: During the time of COVID 19, public health care institutions have used social media to inform and aware society. Aim & Objective: To analyze how Public Health Care Institutes conveyed the health information and messages through social media platform- Twitter during COVID 19, and analyzing its impact through sentiment analysis of comments. Material & Methods: The Thematic and sentiment analysis method has been used to analyze the data of the Twitter handle of AIIMS, Raipur in two phases; January-March 2020, and April-June 2020.  Results: The analysis shows that the sharing of COVID-19 updates on AIIMS, Raipur Twitter handle increased the followers 15 times from 2,000+ in March 2020 to 30,000+ in June 2020, and the sentiment analysis reflects that COVID related updates received 96.7 % positive comments. Conclusion: The case study finds that transparent and informative message sharing through social media by public health care institutions can create an effective channel of communication. This results in a positive institutional image.

scholarly journals Verb Based Sentiment Research

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2468-2471
Keyword(s):  
Sentiment Analysis ◽  
Research Work ◽  
Online Reviews ◽  
Parts Of Speech ◽  
Pos Tagging ◽  
New Strategy ◽  
Semantic Orientation ◽  
The Difference ◽  
Pos Tagger ◽  
Speech Tagging

Sentiment Analysis is one of the leading research work. This paper proposes a model for the description of verbs that provide a structure for developing sentiment analysis. The verbs are very significant language elements and they receive the attention of linguistic researchers. The text is processed for parts-of-speech tagging (POS tagging). With the help of POS tagger, the verbs from each sentence are extracted to show the difference in sentiment analysis values. The work includes performing parts-of-speech tagging to obtain verb words and implement TextBlob and VADER to find the semantic orientation to mine the opinion from the movie review. We achieved interesting results, which were assessed effectively for accuracy by considering with and without verb form words. The findings show that concerning verb words accuracy increases along with emotion words. This introduces a new strategy to classify online reviews using components of algorithms for parts-of-speech..

scholarly journals An assessment of fluoride related posts on Instagram

2019 ◽  
Vol 9 (1) ◽  
pp. 85-88 ◽  
Author(s):  
Corey H. Basch ◽  
Nicole Milano ◽  
Grace C. Hillyer
Keyword(s):  
Social Media ◽  
Health Information ◽  
Driving Force ◽  
English Language ◽  
Fluoride Content ◽  
Conspiracy Theory ◽  
The Internet ◽  
Medical Providers ◽  
Social Media Platform ◽  
Media Platform

Background: Social media is a driving force in the sharing of information. The purpose of this study is to describe fluoride related content on Instagram, a popular social media platform. Methods: Content categories were created and coded to better describe the nature of the posts.Data collection occurred in three sessions, two months apart. Only relevant posts that included images and had text written in the English language were included. Results: The most common topics were conspiracy theory, contained in 37.3% of posts, followed by dangers of fluoride to health (30.3%) and benefits of fluoride to teeth (28.7%). Of the posts reviewed, 96/300 (32.0%) contained pro-fluoride content while 139/300 (63.0%) posts featured anti-fluoride content. Content varied significantly between pro- and anti-fluoride posts.Conclusion: Our review of Instagram posts revealed that there were approximately 300 posts focused on fluoride related content. Of these posts, there was a higher number of anti-fluoride related content compared to pro-fluoride related content. With accessibility comes the potential for misinformation. Future efforts from medical providers need to focus on educating consumers about reliable sources for health information on the internet.

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