scholarly journals Visual Sentiment Analysis on Social Media Data

2021 ◽  
pp. 366-372
Author(s):  
Harshala Bhoir ◽  
K. Jayamalini
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Social Media Data ◽  
Media Image ◽  
The Social ◽  
Textual Data ◽  
Art Works ◽  
Negative Sentiment ◽  
Media Data

Visual sentiment analysis is the way to automatically recognize positive and negative emotions from images, videos, graphics, stickers etc. To estimate the polarity of the sentiment evoked by images in terms of positive or negative sentiment, most of the state-of-the-art works exploit the text associated to a social post provided by the user. However, such textual data is typically noisy due to the subjectivity of the user which usually includes text useful to maximize the diffusion of the social post. Proposed system will extract and employ an Objective Text description of images automatically extracted from the visual content rather than the classic Subjective Text provided by the user. The proposed System will extract three views visual view, subjective text view and objective text view of social media image and will give sentiment polarity positive, negative or neutral based on hypothesis table.

scholarly journals Bi-Lingual (English, Punjabi) Sarcastic Sentiment Analysis by using Classification Methods

2019 ◽  
Vol 8 (9) ◽  
pp. 1383-1388
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Main Step ◽  
Online Data ◽  
Social Media Data ◽  
The Social ◽  
Negative Comments ◽  
Regional Languages ◽  
Media Data ◽  
Day By Day

Sentiment analysis is one of the heated topic in the field of text mining. As the social media data is increased day by day the main need of the data scientists is to classify the data so that it can be further used for decision making or knowledge discovery. Now –a-days everything and everyone available online so to check the latest trends in business or in daily life one must consider the online data. The main focus of sentiment analysis is to focus on positive or negative comments so that a well define picture is created that what is trending or not but the sarcasm manipulates the data as in sarcastic comment negative comment consider as positive because of the presence of positive words in the comment or data so it is necessary to detect the sarcasm in online data . The data on social media is available in various languages so sentiment analysis in regional languages is also a main step . In the proposed work we focus on two languages i.e Punjabi and English. Here we use deep learning based neural networks for the sarcasm detection in English as well as Punjabi language. In the proposed work we consider three datasets i.e. balanced English dataset, Balanced Punjabi Dataset and unbalanced Punjabi dataset. We used six different models to check the accuracy of the classified data the models we used are LSTM with word embedding layer, BiLSTM with , LSTM+LSTM, BiLSTM+BiLSTM, LSTM+BiLSTM, CNN respectively. LSTM provide better accuracy for balanced Punjabi and English dataset i.e. 95.63% and 94.17% respectively. The accuracy for unbalanced Punjabi dataset is provided by BiLSTM i.e.96.31%.

scholarly journals SCRAPING OF SOCIAL MEDIA DATA USING PYTHON-3 AND PERFORMING DATA ANALYTICS USING MICROSOFT POWER BI.

2020 ◽  
Vol 9 (7) ◽  
pp. 66-79
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Data Analytics ◽  
Marketing Strategies ◽  
Social Media Data ◽  
The Social ◽  
The Right ◽  
Media Data ◽  
Course Of Study

The manifestation of humanity is driven by fulfillment of desires. These desires are satiated by the society and its resources. But after the advent of social media the societal boundaries have shrunken but desires haven’t, hence the desires are now fulfilled through social media. The aforementioned phenomenon was recognized by the business plutocrats very early and have started to satisfy human desires using social media as a tool. But before satisfying the desires, the businesses needs to identify the specific desires of an individual. The identification of specific desires/needs will help the marketing agencies to develop user specific marketing strategies. These desires are explicitly available through the expressions of sentiments in the social media. The sentiment analysis can provide an insight to the desires of an individual. These patterns and insights helps the businesses to market their product to the right person. The sentiments and expressions can be captured using the scraping technique. The aforesaid points highlight’s the course of study followed by this paper and it is to perform data analytics of the social media data scraped using python.

Competitive intelligence in social media Twitter: iPhone 6 vs. Galaxy S5

2016 ◽  
Vol 40 (1) ◽  
pp. 42-61 ◽  
Author(s):  
Yoosin Kim ◽  
Rahul Dwivedi ◽  
Jie Zhang ◽  
Seung Ryul Jeong
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Purchase Intention ◽  
Opinion Mining ◽  
Sales Performance ◽  
Competitive Intelligence ◽  
Social Media Data ◽  
Content Type ◽  
The Social ◽  
Media Data

Purpose – The purpose of this paper is to mine competitive intelligence in social media to find the market insight by comparing consumer opinions and sales performance of a business and one of its competitors by analyzing the public social media data. Design/methodology/approach – An exploratory test using a multiple case study approach was used to compare two competing smartphone manufacturers. Opinion mining and sentiment analysis are conducted first, followed by further validation of results using statistical analysis. A total of 229,948 tweets mentioning the iPhone6 or the GalaxyS5 have been collected for four months following the release of the iPhone6; these have been analyzed using natural language processing, lexicon-based sentiment analysis, and purchase intention classification. Findings – The analysis showed that social media data contain competitive intelligence. The volume of tweets revealed a significant gap between the market leader and one follower; the purchase intention data also reflected this gap, but to a less pronounced extent. In addition, the authors assessed whether social opinion could explain the sales performance gap between the competitors, and found that the social opinion gap was similar to the shipment gap. Research limitations/implications – This study compared the social media opinion and the shipment gap between two rival smart phones. A business can take the consumers’ opinions toward not only its own product but also toward the product of competitors through social media analytics. Furthermore, the business can predict market sales performance and estimate the gap with competing products. As a result, decision makers can adjust the market strategy rapidly and compensate the weakness contrasting with the rivals as well. Originality/value – This paper’s main contribution is to demonstrat the competitive intelligence via the consumer opinion mining of social media data. Researchers, business analysts, and practitioners can adopt this method of social media analysis to achieve their objectives and to implement practical procedures for data collection, spam elimination, machine learning classification, sentiment analysis, feature categorization, and result visualization.

scholarly journals Using Social Media in Tourist Sentiment Analysis: A Case Study of Andalusia during the Covid-19 Pandemic

2021 ◽  
Vol 13 (7) ◽  
pp. 3836
Author(s):  
David Flores-Ruiz ◽  
Adolfo Elizondo-Salto ◽  
María de la O. Barroso-González
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Cost Savings ◽  
Mass Scale ◽  
Tourism Sector ◽  
The Social ◽  
Analytical Tools ◽  
Media Data

This paper explores the role of social media in tourist sentiment analysis. To do this, it describes previous studies that have carried out tourist sentiment analysis using social media data, before analyzing changes in tourists’ sentiments and behaviors during the COVID-19 pandemic. In the case study, which focuses on Andalusia, the changes experienced by the tourism sector in the southern Spanish region as a result of the COVID-19 pandemic are assessed using the Andalusian Tourism Situation Survey (ECTA). This information is then compared with data obtained from a sentiment analysis based on the social network Twitter. On the basis of this comparative analysis, the paper concludes that it is possible to identify and classify tourists’ perceptions using sentiment analysis on a mass scale with the help of statistical software (RStudio and Knime). The sentiment analysis using Twitter data correlates with and is supplemented by information from the ECTA survey, with both analyses showing that tourists placed greater value on safety and preferred to travel individually to nearby, less crowded destinations since the pandemic began. Of the two analytical tools, sentiment analysis can be carried out on social media on a continuous basis and offers cost savings.

The Effects of Different Kernels in SVM Sentiment Analysis on Mass Social Distancing

2020 ◽  
Vol 9 (2) ◽  
pp. 161
Author(s):  
Komang Dhiyo Yonatha Wijaya ◽  
Anak Agung Istri Ngurah Eka Karyawati
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Kernel Method ◽  
Idle Time ◽  
Linear Kernel ◽  
Social Distancing ◽  
The Social ◽  
Negative Sentiment ◽  
F Measure ◽  
Kernel Yield

During this pandemic, social media has become a major need as a means of communication. One of the social medias used is Twitter by using messages referred to as tweets. Indonesia currently undergoing mass social distancing. During this time most people use social media in order to spend their idle time However, sometimes, this result in negative sentiment that used to insult and aimed at an individual or group. To filter that kind of tweets, a sentiment analysis was performed with SVM and 3 different kernel method. Tweets are labelled into 3 classes of positive, neutral, and negative. The experiments are conducted to determine which kernel is better. From the sentiment analysis that has been performed, SVM linear kernel yield the best score Some experiments show that the precision of linear kernel is 57%, recall is 50%, and f-measure is 44%

Comparison of different neural networks in sentiment analysis of social media data

2021 ◽  
Author(s):  
Vadim Moshkin ◽  
Andrew Konstantinov ◽  
Nadezhda Yarushkina ◽  
Alexander Dyrnochkin
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Social Media Data ◽  
Media Data

scholarly journals Using social media data to map the areas most affected by ISIS in Syria

2020 ◽  
Vol 26 (1) ◽  
pp. 464-470
Author(s):  
Mohamad Hasan
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Geographical Information ◽  
Data Mapping ◽  
Islamic State ◽  
Social Media Data ◽  
The Social ◽  
Mapping Process ◽  
Data Source ◽  
Media Data

This paper presents a model to collect, save, geocode, and analyze social media data. The model is used to collect and process the social media data concerned with the ISIS terrorist group (the Islamic State in Iraq and Syria), and to map the areas in Syria most affected by ISIS accordingly to the social media data. Mapping process is assumed automated compilation of a density map for the geocoded tweets. Data mined from social media (e.g., Twitter and Facebook) is recognized as dynamic and easily accessible resources that can be used as a data source in spatial analysis and geographical information system. Social media data can be represented as a topic data and geocoding data basing on the text of the mined from social media and processed using Natural Language Processing (NLP) methods. NLP is a subdomain of artificial intelligence concerned with the programming computers to analyze natural human language and texts. NLP allows identifying words used as an initial data by developed geocoding algorithm. In this study, identifying the needed words using NLP was done using two corpora. First corpus contained the names of populated places in Syria. The second corpus was composed in result of statistical analysis of the number of tweets and picking the words that have a location meaning (i.e., schools, temples, etc.). After identifying the words, the algorithm used Google Maps geocoding API in order to obtain the coordinates for posts.

scholarly journals Sentiment Analysis in Social Media using Machine Learning Techniques

2020 ◽  
pp. 193-201 ◽  
Author(s):  
Hayder A. Alatabi ◽  
Ayad R. Abbas
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Machine Learning Techniques ◽  
Great Success ◽  
Social Media Data ◽  
Learning Techniques ◽  
The World ◽  
Analysis System ◽  
Media Data

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.

Product Review Analysis Using Social Media Data Based on Sentiment Analysis

2020 ◽  
pp. 527-533
Author(s):  
S. M. Mazharul Hoque Chowdhury ◽  
Sheikh Abujar ◽  
Ohidujjaman ◽  
Khalid Been Md. Badruzzaman ◽  
Syed Akhter Hossain
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Product Review ◽  
Social Media Data ◽  
Review Analysis ◽  
Media Data

Employing the Sentiment Analysis Tool in NVivo 11 Plus on Social Media Data

2017 ◽  
pp. 175-244
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Analysis Tool ◽  
Attitudes And Beliefs ◽  
Social Media Data ◽  
Public Issues ◽  
Public Events ◽  
Brand Reputation ◽  
Text Corpora ◽  
Media Data

Sentiment analysis has been used to assess people's feelings, attitudes, and beliefs, ranging from positive to negative, on a variety of phenomena. Several new autocoding features in NVivo 11 Plus enable the capturing of sentiment analysis and extraction of themes from text datasets. This chapter describes eight scenarios in which these tools may be applied to social media data, to (1) profile egos and entities, (2) analyze groups, (3) explore metadata for latent public conceptualizations, (4) examine trending public issues, (5) delve into public concepts, (6) observe public events, (7) analyze brand reputation, and (8) inspect text corpora for emergent insights.

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