Sentiment Analysis of Movies on Social Media using R Studio

This paper presents sentiment analysis of twitter data on movies using R-studio. Twitter is one of the largest social media that shares user opinion about a thing or event that happens all around the world. Recently social media analysis gained importance in digital marketing. User tweets about a product or event, person, movie, etc., are analyzed to know market trends and customer feedback. In this paper, first we have performed literature study on various methods used in twitter data analysis. Second, we have discussed about the steps involved in accessing twitter data. Finally, we have performed sentiment analysis on tweeter data for the movies titled kabali, Bharath Ane Nenu Mersal, and Dangal. User data for the movies are classified into positive, neutral and negative based on DBM and SVM. Sentiment scores are used as evaluation metrics. Results shows DBM is effective in classifying sentiments and produced better sentiment scores compared to SVM. Results are helpful in identifying popularity of the movies and audience feedback about the movies.

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Predicting the French Stock Market Using Social Media Analysis

International Journal of Virtual Communities and Social Networking ◽

10.4018/ijvcsn.2015040104 ◽

2015 ◽

Vol 7 (2) ◽

pp. 70-84

Author(s):

Vincent Martin ◽

Emmanuel Bruno ◽

Elisabeth Murisasco

Keyword(s):

Neural Network ◽

Social Media ◽

Stock Market ◽

Sentiment Analysis ◽

Media Analysis ◽

Percentage Error ◽

Social Media Analysis ◽

Single Output ◽

The Neural Network ◽

Simple Neural Network

In this article, the authors try to predict the next-day CAC40 index. They apply the idea of Johan Bollen et al. from (Bollen, Mao, & Zeng, 2011) on the French stock market and they conduct their experiment using French tweets. Two analyses are applied on tweets: sentiment analysis and subjectivity analysis. Results of these analyses are then used to train a simple neural network. The input features are the sentiment, the subjectivity and the CAC40 closing value at day-1 and day-0. The single output value is the predicted CAC40 closing value at day+1. The authors propose an architecture using the JEE framework resulting in a better scalability and an easier industrialization. The main experiments are conducted over 5 months of data. The authors train their neural network on the first of the data and they test predictions on the remaining quarter. Their best run gives a direction accuracy of 80% and a mean absolute percentage error (MAPE) of 2.97%. In another experiment, the authors retrain the neural network each day which decreases the MAPE to 1.14%.

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Social Media Analysis of User’s Responses to Terrorism Using Sentiment Analysis and Text Mining

Procedia Computer Science ◽

10.1016/j.procs.2018.10.297 ◽

2018 ◽

Vol 140 ◽

pp. 95-103 ◽

Cited By ~ 6

Author(s):

Samah Mansour

Keyword(s):

Social Media ◽

Text Mining ◽

Sentiment Analysis ◽

Media Analysis ◽

Social Media Analysis

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Creating Brand Image Profile by Social Media Analysis

EMAJ Emerging Markets Journal ◽

10.5195/emaj.2021.228 ◽

2021 ◽

Vol 11 (2) ◽

pp. 8-15

Author(s):

İbrahim Sabuncu ◽

Berivan Edeş ◽

Doruk Sıtkıbütün ◽

İlayda Girgin ◽

Kadir Zehir

Keyword(s):

Artificial Intelligence ◽

Social Media ◽

Sentiment Analysis ◽

Brand Image ◽

Media Analysis ◽

Alternative Methods ◽

Social Media Analysis ◽

Focus Group Study ◽

Complementary Method ◽

Mining Tool

The purpose of creating a brand image profile is to measure the brand perception of consumers considering brand attributes. Thus, marketing decisions can be made based on the brand's strengths and weaknesses by determining them. The brand image profile is traditionally created using the attitude scales and surveys. However, alternative methods are needed since the questionnaires' responses are careless, the number of participants is relatively low and the cost per participant is high. In this study, as an alternative method, creating a brand image profile by analyzing social media data with artificial intelligence was made for the iPhone product. Firstly, the focus group study determined the attributes related to the last version of the iPhone. Then, between December 17th, 2019 and March 23rd, 2020, 87.227 tweets that include these attributes in English were collected from the Twitter social media platform through the RapidMiner data mining tool. Sentiment analysis was performed on collected tweets by the MeaningCloud text mining tool. In this analysis, positive and negative emotions were tried to be detected through artificial intelligence algorithms. Net Brand Reputation Score (NBR) was calculated using the positive and negative tweets amount for each attribute separately. Brand image profile was created by skew analysis using NBR values. As a result, it is thought that social media analysis can be a complementary method that can be used with traditional methods in creating a brand image profile. So, it is seen as an inevitable method to use in further studies to make sentiment analysis by processing raw data received from the Social Media platforms through artificial intelligence algorithms to transform the product label or the perspectives of an event into meaningful information.

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Literature review on Real-time Location-Based Sentiment Analysis on Twitter

Advances in Technology ◽

10.31357/ait.v1i2.4936 ◽

2021 ◽

Vol 1 (2) ◽

Author(s):

Dilmini Rathnayaka ◽

Pubudu K.P.N Jayasena ◽

Iraj Ratnayake

Keyword(s):

Social Media ◽

Data Analysis ◽

Sentiment Analysis ◽

Real Time ◽

Analysis Data ◽

Time Data ◽

Twitter Data ◽

Analysis Methods ◽

Real Time Data ◽

Twitter Data Analysis

Sentiment analysis mainly supports sorting out the polarity and provides valuable information with the use of raw data in social media platforms. Many fields like health, business, and security require real-time data analysis for instant decision-making situations.Since Twitter is considered a popular social media platform to collect data easily, this paper is considering data analysis methods of Twitter data, real-time Twitter data analysis based on geo-location. Twitter data classification and analysis can be done with the use of diverse algorithms and deciding the most appropriate algorithm for data analysis, can be accomplished by implementing and testing these diverse algorithms.This paper is discussing the major description of sentiment analysis, data collection methods, data pre-processing, feature extraction, and sentiment analysis methods related to Twitter data. Real-time data analysis arises as a major method of analyzing the data available online and the real-time Twitter data analysis process is described throughout this paper. Several methods of classifying the polarized Twitter data are discussed within the paper while depicting a proposed method of Twitter data analyzing algorithm. Location-based Twitter data analysis is another crucial aspect of sentiment analyses, that enables data sorting according to geo-location, and this paper describes the way of analyzing Twitter data based on geo-location. Further, a comparison about several sentiment analysis algorithms used by previous researchers has been reported and finally, a conclusion has been provided.

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Aspect-based Sentiment Analysis using Dependency Parsing

ACM Transactions on Asian and Low-Resource Language Information Processing ◽

10.1145/3485243 ◽

2022 ◽

Vol 21 (3) ◽

pp. 1-19

Author(s):

Sujata Rani ◽

Parteek Kumar

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Minimum Distance ◽

Dependency Graph ◽

Media Analysis ◽

Product Reviews ◽

Dependency Parsing ◽

Social Media Analysis ◽

Dependency Parser ◽

Sentiment Word

In this paper, an aspect-based Sentiment Analysis (SA) system for Hindi is presented. The proposed system assigns a separate sentiment towards the different aspects of a sentence as well as it evaluates the overall sentiment expressed in a sentence. In this work, Hindi Dependency Parser (HDP) is used to determine the association between an aspect word and a sentiment word (using Hindi SentiWordNet) and works on the idea that closely connected words come together to express a sentiment about a certain aspect. By generating a dependency graph, the system assigns the sentiment to an aspect having a minimum distance between them and computes the overall polarity of the sentence. The system achieves an accuracy of 83.2% on a corpus of movie reviews and its results are compared with baselines as well as existing works on SA. From the results, it has been observed that the proposed system has the potential to be used in emerging applications like SA of product reviews, social media analysis, etc.

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A Twitter-Lived Red Tide Crisis on Chiloé Island, Chile: What Can Be Obtained for Social-Ecological Research through Social Media Analysis?

Sustainability ◽

10.3390/su12208506 ◽

2020 ◽

Vol 12 (20) ◽

pp. 8506

Author(s):

Aldo Mascareño ◽

Pablo A. Henríquez ◽

Marco Billi ◽

Gonzalo A. Ruz

Keyword(s):

Social Media ◽

Meaning Making ◽

Red Tide ◽

Principal Component ◽

Media Analysis ◽

Ecological Crisis ◽

Social Media Analysis ◽

Sequence Of Events ◽

Social Ecological ◽

Twitter Data

Considering traditional research on social-ecological crises, new social media analysis, particularly Twitter data, contributes with supplementary exploration techniques. In this article, we argue that a social media approach to social-ecological crises can offer an actor-centered meaningful perspective on social facts, a depiction of the general dynamics of meaning making that takes place among actors, and a systemic view of actors’ communication before, during and after the crisis. On the basis of a multi-technique approach to Twitter data (TF-IDF, hierarchical clustering, egocentric networks and principal component analysis) applied to a red tide crisis on Chiloé Island, Chile, in 2016, the most significant red tide in South America ever, we offer a view on the boundaries and dynamics of meaning making in a social-ecological crisis. We conclude that this dynamics shows a permanent reflexive work on elucidating the causes and effects of the crisis that develops according to actors’ commitments, the sequence of events, and political conveniences. In this vein, social media analysis does not replace good qualitative research, it rather opens up supplementary possibilities for capturing meanings from the past that cannot be retrieved otherwise. This is particularly relevant for studying social-ecological crises and supporting collective learning processes that point towards increased resilience capacities and more sustainable trajectories in affected communities.

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Innovative Approaches in Sports Science—Lexicon-Based Sentiment Analysis as a Tool to Analyze Sports-Related Twitter Communication

Applied Sciences ◽

10.3390/app10020431 ◽

2020 ◽

Vol 10 (2) ◽

pp. 431

Author(s):

Fabian Wunderlich ◽

Daniel Memmert

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Public Interest ◽

Online Communication ◽

Media Analysis ◽

Social Media Analysis ◽

Sports Science ◽

Sports Events ◽

Research Areas ◽

Textual Data

Sentiment analysis refers to the algorithmic extraction of subjective information from textual data and—driven by the increasing amount of online communication—has become one of the fastest growing research areas in computer science with applications in several domains. Although sports events such as football matches are accompanied by a huge public interest and large amount of related online communication, social media analysis in general and sentiment analysis in particular are almost unused tools in sports science so far. The present study tests the feasibility of lexicon-based tools of sentiment analysis with regard to football-related textual data on the microblogging platform Twitter. The sentiment of a total of 10,000 tweets with reference to ten top-level football matches was analyzed both manually by human annotators and algorithmically by means of publicly available sentiment analysis tools. Results show that the general sentiment of realistic sets (1000 tweets with a proportion of 60% having the same polarity) can be classified correctly with more than 95% accuracy. The present paper demonstrates that sentiment analysis can be an effective and useful tool for sports-related content and is intended to stimulate the increased use of and discussion on sentiment analysis in sports science.

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Social media analysis of Twitter tweets related to ASD in 2019-2020, with particular attention to Covid-19: topic modelling and sentiment analysis (Preprint)

10.2196/preprints.35043 ◽

2021 ◽

Author(s):

Luca Corti ◽

Michele Zanetti ◽

Giovanni Tricella ◽

Maurizio Bonati

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Factorization Method ◽

Autism Spectrum ◽

Media Analysis ◽

Social Media Analysis ◽

Related Information ◽

Data Collection Process ◽

Use Of Social Media ◽

Negative Sentiment

BACKGROUND Social media contains an overabundance of health information relating to people living with different type of diseases. Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with lifelong impacts and reported trends have revealed a considerable increase in prevalence and incidence. Research had shown that the ASD community provides significant support to its members through Twitter, providing information about their values and perceptions through their use of words and emotional stance. OBJECTIVE Our purpose was to analyze the messages posted on Twitter platform regarding ASD and analyze the topics covered within the tweets, in order to understand the attitude of the various people interested in the topic. In particular, we focused on the discussion of ASD and Covid-19. METHODS The data collection process was based on the search for tweets through hashtags and keywords. After bots screening, the NMF (Non-Negative Matrix Factorization) method was used for topic modeling because it produces more coherent topics compared to other solutions. Sentiment scores were calculated using AFiNN for each tweet to represent its negative to positive emotion. RESULTS From the 2.458.929 tweets produced in 2020, 691.582 users were extracted (188 bots which generated 59.104 tweets), while from the 2.393.236 total tweets from 2019, the number of identified users was 684.032 (230 bots which generated 50.057 tweets). The number of tweets and the topics covered are very similar between 2019 and 2020. The total number of Covid-asd tweets is only a small part of the total dataset. Often, the negative sentiment identified in the sentiment analysis referred to anger towards Covid-19 and its management, while the positive sentiment reflected the necessity to provide constant support to people with ASD. CONCLUSIONS Social media contributes to a great discussion on topics related to autism, especially with regards to focus on family, community, and therapies. The Covid-19 pandemic increased the use of social media, especially during the lockdown period. It is important to help develop and distribute appropriate, evidence-based ASD-related information.

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