Aspect-based Sentiment Analysis using Dependency Parsing

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.

Predicting the French Stock Market Using Social Media Analysis

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%.

scholarly journals Creating Brand Image Profile by Social Media Analysis

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.

scholarly journals Sentiment Analysis of Movies on Social Media using R Studio

2019 ◽  
Vol 8 (6) ◽  
pp. 2171-2175
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Media Analysis ◽  
Literature Study ◽  
Social Media Analysis ◽  
Customer Feedback ◽  
Twitter Data ◽  
User Data ◽  
Market Trends ◽  
Twitter Data Analysis

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.

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

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.

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

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.

scholarly journals A Tale of Two Disasters

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 694-694
Author(s):  
Tammy Mermelstein
Keyword(s):  
Older Adults ◽  
Social Media ◽  
Interest Group ◽  
Media Analysis ◽  
Ice Storm ◽  
Social Media Analysis ◽  
Individual Survival ◽  
Social Media Platforms

Abstract Preparing for or experiencing a disaster is never easy, but how leaders communicate with older adults can ease a situation or make it exponentially worse. This case study describes two disasters in the same city: Hurricane Harvey and the 2018 Houston Texas Ice Storm and the variation in messaging provided to and regarding older adults. For example, during Hurricane Harvey, the primary pre-disaster message was self-preparedness. During the storm, messages were also about individual survival. Statements such as “do not [climb into your attic] unless you have an ax or means to break through,” generated additional fear for older adults and loved ones. Yet, when an ice storm paralyzed Houston a few months later, public messaging had a strong “check on your elderly neighbors” component. This talk will explore how messaging for these events impacted older adults through traditional and social media analysis, and describe how social media platforms assisted people with rescue and recovery. Part of a symposium sponsored by Disasters and Older Adults Interest Group.

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