scholarly journals Twitter Sentimental Analysis

2020 ◽  
Vol 6 (12) ◽  
pp. 355-359
Author(s):  
Aditya Prakash
Keyword(s):  
Public Opinion ◽  
Social Behavior ◽  
Unsupervised Learning ◽  
Sentiment Analysis ◽  
Word Embedding ◽  
Statistical Characteristics ◽  
Great Scope

Twitter sentiment analysis (TSA) provides the methods to survey public emotions about the products or events associated with them. Categorization of opinions through tweets involves a great scope of study and may yield interesting results and insights on public opinion and social behavior towards different events, services, product, geopolitical issues, situations and scenarios that concern mankind at large. These attributes are expressed explicitly through emoticons, exclamation, sentiment words and so on. In this paper, we introduce a word embedding (Word2Vec) technique obtained by unsupervised learning built on large twitter corpora, this process uses co-occurrence statistical characteristics between words in tweets and hidden contextual semantic interrelation

Covid-19 Vaccine Tweets Sentiment Analysis and Topic Modelling for Public Opinion Mining

2021 ◽  
Author(s):  
Trisha Baldha ◽  
Malvi Mungalpara ◽  
Priyanka Goradia ◽  
Santosh Bharti
Keyword(s):  
Public Opinion ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Topic Modelling

A Comparative Study on Sentiment Analysis Influencing Word Embedding Using SVM and KNN

2021 ◽  
pp. 199-211
Author(s):  
Bachchu Paul ◽  
Sanchita Guchhait ◽  
Tanushree Dey ◽  
Debashri Das Adhikary ◽  
Somnath Bera
Keyword(s):  
Comparative Study ◽  
Sentiment Analysis ◽  
Word Embedding

Semisupervised sentiment analysis method for online text reviews

2020 ◽  
pp. 016555152091003
Author(s):  
Gyeong Taek Lee ◽  
Chang Ouk Kim ◽  
Min Song
Keyword(s):  
Unsupervised Learning ◽  
Sentiment Analysis ◽  
Supervised Learning ◽  
Model Space ◽  
Training Dataset ◽  
Learning Approach ◽  
Learning Models ◽  
Text Data ◽  
Learning Techniques ◽  
Sentiment Dictionary

Sentiment analysis plays an important role in understanding individual opinions expressed in websites such as social media and product review sites. The common approaches to sentiment analysis use the sentiments carried by words that express opinions and are based on either supervised or unsupervised learning techniques. The unsupervised learning approach builds a word-sentiment dictionary, but it requires lengthy time periods and high costs to build a reliable dictionary. The supervised learning approach uses machine learning models to learn the sentiment scores of words; however, training a classifier model requires large amounts of labelled text data to achieve a good performance. In this article, we propose a semisupervised approach that performs well despite having only small amounts of labelled data available for training. The proposed method builds a base sentiment dictionary from a small training dataset using a lasso-based ensemble model with minimal human effort. The scores of words not in the training dataset are estimated using an adaptive instance-based learning model. In a pretrained word2vec model space, the sentiment values of the words in the dictionary are propagated to the words that did not exist in the training dataset. Through two experiments, we demonstrate that the performance of the proposed method is comparable to that of supervised learning models trained on large datasets.

Crowdsourcing Investigations

2014 ◽  
Vol 6 (4) ◽  
pp. 60-75 ◽  
Author(s):  
Andrea H. Tapia ◽  
Nicolas J. LaLone
Keyword(s):  
Public Opinion ◽  
Sentiment Analysis ◽  
Ethical Dilemmas ◽  
The Internet ◽  
Social Approval ◽  
Online Groups ◽  
The Public ◽  
Large Numbers ◽  
Boston Marathon Bombing ◽  
Boston Marathon

In this paper the authors illustrate the ethical dilemmas that arise when large public investigations in a crisis are crowdsourced. The authors focus the variations in public opinion concerning the actions of two online groups during the immediate aftermath of the Boston Marathon Bombing. These groups collected and organized relief for victims, collected photos and videos taken of the bombing scene and created online mechanisms for the sharing and analysis of images collected online. They also used their large numbers and the affordances of the Internet to produce an answer to the question, “who was the perpetrator, and what kind of bomb was used?” The authors view their actions through public opinion, through sampling Twitter and applying a sentiment analysis to this data. They use this tool to pinpoint moments during the crisis investigation when the public became either more positively or negatively inclined toward the actions of the online publics. The authors use this as a surrogate, or proxy, for social approval or disapproval of their actions, which exposes large swings in public emotion as ethical lines are crossed by online publics.

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