Identifying the Overlap between Election Result and Candidates’ Ranking Based on Hashtag-Enhanced, Lexicon-Based Sentiment Analysis

Rezapour, Rezvaneh; Wang, Lufan; Abdar, Omid; Diesner, Jana

doi:10.1109/icsc.2017.92

Cited by 41 publications

(22 citation statements)

References 5 publications

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“…We plan to extract the text and hashtags related to films and add them as the sentimental and/or topical word to our features to expand and improve the prediction models. Hashtags were used in previous study in social media analysis for topic modeling [38], sentiment analysis [34], and opinion mining [23]. Numbers of research leveraged social media information to predict a movies success [21].…”

Section: Resultsmentioning

confidence: 99%

Estimating the Rating of the Reviews Based on the Text

Kavousi

Saadatmand

2018

Data Analytics and Learning

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User-generated texts such as reviews and social media are valuable sources of information. Online reviews are important assets for users to buy a product, see a movie, or make a decision. Therefore, rating of a review is one of the reliable factors for all users to read and trust the reviews. This paper analyzes the texts of the reviews to evaluate and predict the ratings. Moreover, we study the effect of lexical features generated from text as well as sentimental words on the accuracy of rating prediction. Our analysis show that words with high information gain score are more efficient compared to words with high TF-IDF value. In addition, we explore the best number of features for predicting the ratings of the reviews.

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Section: Resultsmentioning

confidence: 99%

Estimating the Rating of the Reviews Based on the Text

Kavousi

Saadatmand

2018

Data Analytics and Learning

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“…In the 2016 US election, manually annotated corpus-based hash tags along with negation deletion were used a 7% increase in accuracy level was observed. [6] A different approach is proposed for determining election results before the actual election took place. The proposal includes taking into consideration user influence factor, along with applying SVM algorithm.…”

Section: Literature Surveymentioning

confidence: 99%

Text Polarity Detection using Multiple Supervised Machine Learning Algorithms

Kar*,

Saha,

Biswas

2020

IJITEE

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Sentiment analysis is the classifying of a review, opinion or a statement into categories, which brings clarity about specific sentiments of customers or the concerned group to businesses and developers. These categorized data are very critical to the development of businesses and understanding the public opinion. The need for accurate opinion and large-scale sentiment analysis on social media platforms is growing day by day. In this paper, a number of machine learning algorithms are trained and applied on twitter datasets and their respective accuracies are determined separately on different polarities of data, thereby giving a glimpse to which algorithm works best and which works worst..

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“…Jyoti Ramteke et al [12] first collected the data using the Twitter API and stored them in CSV file, then pre-processed it to remove specific characteristics and URLs and then hand-held data marking using the Hashtag Label, and then a VADER tool based on the Lexicon and the Regulative Sentiment Analysis Tool and introduced a scalable machine learning model to predict the election outcomes using two-stage frameworks to create training data from twitter data without negotiating on features and context. The sentiment analysis usually takes place on three stages: document-based, sentence-based and aspect-based.…”

Section: Literature Surveymentioning

confidence: 99%

“…Social media like Twitter, My Space, Facebook, Instagram. Platforms like LinkedIn, Facebook, Twitter, Instagram, My Space, Tumbler and Google+ are being profoundly used to share opinions, reviews, suggestions, and ratings [12].…”

Section: Introductionmentioning

confidence: 99%

Predicting Election Results using NLTK

Kameswari*,

Raghaveni,

Shankar

et al. 2019

IJITEE

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In today’s world, people are usually using social media networks for trying to communicate with other users and for sharing information across the world. The online social networking sites have become considerable tools and are providing a common medium for a number of users to communicate with each other. Twitter is the most prominent microblogging website and one among the social networking sites that grow on a daily basis. Social media incorporates an extensive amount of data in the form of tweets, forums, status updates, comments, etc. in an attempt to automatically process and analyze these data, applications can rely on analysis approaches such as sentiment analysis. Twitter sentiment analysis is an application of sentiment analysis on data from Twitter (tweets), to obtain user's opinions and sentiments. Natural Language Toolkit (NLTK) is a library based on machine learning methods in python & sentiment analysis tool. Which provides the base for text processing and classification? The research work proposed a machine learning-based classifier to extract the tweets on elections and analyze the opinion of the tweeples (people who use twitter). The tweets can be categorized as positive, negative and neutral towards a particular politician. We classify these processed tweets using a supervised machine learning classification approach. The classifier used to classify the tweets as positive, negative or neutral is Naive Bayes Classifier. The classifier is trained with tweets bearing a distinctive polarity. The percentage of positive and negative tweets is then measured and graphically represented.

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Identifying the Overlap between Election Result and Candidates’ Ranking Based on Hashtag-Enhanced, Lexicon-Based Sentiment Analysis

Cited by 41 publications

References 5 publications

Estimating the Rating of the Reviews Based on the Text

Estimating the Rating of the Reviews Based on the Text

Text Polarity Detection using Multiple Supervised Machine Learning Algorithms

Predicting Election Results using NLTK

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