scholarly journals Ontology-based Sentiment Analysis for Brand Crisis Detection on Online Social Media

2020 ◽  
Vol 3 (SI1) ◽  
pp. First
Author(s):  
Trung Đức Mai ◽  
Tho Thanh Quan
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
High Frequency ◽  
Online Environment ◽  
High Frequencies ◽  
Online Social Media ◽  
Analysis Technique ◽  
Negative Comments ◽  
Brand Crisis ◽  
Do So

This paper discusses detection of brand crisis on online social media, i.e. when a brand is being suffered from unexpectedly high frequency of negative comments on online channels such as social networks, electronic news, blog and forum. In order to do so, we combined the usage of probabilistic model for burst detection with ontology-based aspect-level sentiment analysis technique to detect negative mention. The burst on online environment is a trendy topic that is rapidly growing recently.  Thus, a burst with high frequencies of negative mentions to a brand implies a potential online crisis occurring with that brand. Our experimental results show that the aspect-level sentiment analysis technique is extremely useful for detecting of negative mentions that related with the products and brands.

Impact of Deep Learning on Semantic Sentiment Analysis

2021 ◽  
pp. 97-117
Author(s):  
Neha Gupta ◽  
Rashmi Agrawal
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Semantic Technologies ◽  
Sources Of Information ◽  
Accurate Analysis ◽  
Online Social Media ◽  
Semantic Orientation ◽  
Semantically Enhanced

Online social media (forums, blogs, and social networks) are increasing explosively, and utilization of these new sources of information has become important. Semantics plays a significant role in accurate analysis of an emotion speech context. Adding to this area, the already advanced semantic technologies have proven to increase the precision of the tests. Deep learning has emerged as a prominent machine learning technique that learns multiple layers or data characteristics and delivers state-of-the-art output. Throughout recent years, deep learning has been widely used in the study of sentiments, along with the growth of deep learning in many other fields of use. This chapter will offer a description of deep learning and its application in the analysis of sentiments. This chapter will focus on the semantic orientation-based approaches for sentiment analysis. In this work, a semantically enhanced methodology for the annotation of sentiment polarity in Twitter/ Facebook data will be presented.

Sentiment Analysis on Movie Reviews Using Twitter

2020 ◽  
Vol 17 (7) ◽  
pp. 2869-2875
Author(s):  
Sajay Thomas Samuel ◽  
Booma Poolan Marikannan
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Learning Algorithm ◽  
Instant Messaging ◽  
Machine Learning Algorithms ◽  
Depth Information ◽  
Implementation Phase ◽  
Online Social Media ◽  
Past Data

Machine learning can help people to perform complex tasks and solve problems as it uses historical data to learn its pattern and make predictions based on the past data. This research addresses the problem about movie reviews on social media specifically Twitter; where it will gather the tweets on movie reviews and display a rating based on the sentiment of the tweet. Twitter is an online social media website where people from all walks of life communicate by tweeting short updates without exceeding the character limit which is 240 characters. Twitter is continuously growing as a business and became one of the biggest platform for communication and instant messaging. Due to the large number of users, there are voluminous amounts of data available that can be used for more in depth information and insights and to get the sentiments from analysing the tweets. In today’s world, there are many applications that are using sentiment analysis in various fields such as to gets insights about a particular brand or product. To do sentiment analysis using the traditional ways can be time consuming and becomes very complex. The aim of this research is to investigate about the domain of sentiment analysis and incorporate a machine learning algorithm to create a system that is able to get and display the ratings of a particular movie. The machine learning algorithms used are Naïve Bayes Classifier and SVM. The algorithm with better accuracy will be chosen for the implementation phase.

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 Brand crisis in social media: case study using sentiment analysis

Pressacademia ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 246-254
Author(s):  
Abdullah Onden ◽  
Meltem Kiygi-Calli ◽  
Elif Yolbulan-Okan
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Brand Crisis

scholarly journals Social Network Mental Disorders Detection via Online Social Media Mining

2021 ◽  
Vol 7 (01) ◽  
pp. 27-30
Author(s):  
Prof. Narinder Kaur and Lakshay Monga
Keyword(s):  
Social Media ◽  
Social Network ◽  
Mental Disorders ◽  
Mental Disorder ◽  
Sentiment Analysis ◽  
Prototype System ◽  
Social Media Mining ◽  
Online Social Media ◽  
Analyse Data ◽  
Media Mining

Social Network Mental Disorder Detection” or “SNMD” is an approach to analyse data and retrieve sentiment that it embodies. Twitter SNMD analysis is an application of sentiment analysis on data from Twitter (tweets), in order to extract sentiments conveyed by the user. In this paper, we aim to review some papers regarding research in sentiment analysis on Twitter, describing the methodologies adopted and models applied, along with describing a generalized Python based approach. A prototype system is developed and tested.

scholarly journals Reputational Threats Online: Social Media as a Simultaneous Agent of Crisis and Tool for Response and Resolution in the Case Study of an American Academic Library

2018 ◽  
Vol 1 ◽  
pp. 24-26 ◽  
Author(s):  
Margaret Stewart ◽  
Maria Atilano
Keyword(s):  
Social Media ◽  
Crisis Communication ◽  
Online Community ◽  
Academic Library ◽  
Learning Opportunity ◽  
Online Social Media ◽  
Negative Comments ◽  
American Academic

This paper details a reputational threat to an American academic library where a viral social media post and associated negative comments misrepresented the institution and brand’s values. Immediately, the marketing librarian responsible for social media responded to the threat by engaging directly with the library consumers, sharing content and information with the broader online community, and reinforcing the library’s values and commitment to consumers. While the resolution to the crisis was mostly favourable, the event was unanticipated and invited a keen learning opportunity that is documented in this case study. Reflections and takeaways from this incident are discussed in the context of emerging literature on crisis communication, reputation threats, and social media.

A spatiotemporal approach for social media sentiment analysis

First Monday ◽  
2021 ◽  
Author(s):  
Andre Alves ◽  
Cláudio de Souza Baptista ◽  
Davi Oliveira Serrano de Andrade ◽  
Maxwell Guimarães De Oliveira ◽  
Aillkeen Bezerra De Oliveira
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Temporal Analysis ◽  
Accurate Analysis ◽  
Opinion Change ◽  
Flow Maps ◽  
Spatial And Temporal Analysis ◽  
Analysis Technique ◽  
Temporal Dimensions ◽  
Media Sentiment

The rapid growth of user-generated unstructured data through social media has raised several challenges and research opportunities. These data constitute a rich source of information for sentiment analysis and help the understanding of spontaneously expressed opinions. In the past few years, many scientific proposals have addressed sentiment analysis issues. However, most of them do not take into account both spatial and temporal dimensions, which would enable a more accurate analysis. To the best of our knowledge, this approach has not received much attention in the literature. In this article, we formalized a spatiotemporal sentiment analysis technique and applied this technique to a case study of tweets about the FIFA 2014 World Cup. Our approach exploits the summarization of sentiment analysis using the spatial and temporal dimensions and automatically generates opinion change flow maps through both dimensions. The results enable the tracking of opinion change flow maps through spatial and temporal analysis.

scholarly journals Sentiment Analysis Using Random Forest Algorithm-Online Social Media Based

2019 ◽  
Vol 2 (2) ◽  
pp. 29
Author(s):  
Nfn Bahrawi
Keyword(s):  
Social Media ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Community Services ◽  
Random Forest Algorithm ◽  
The Public ◽  
Online Social Media ◽  
Accuracy Of Measurements

Every day billions of data in the form of text flood the internet be it sourced from forums, blogs, social media, or review sites. With the help of sentiment analysis, previously unstructured data can be transformed into more structured data and make this data important information. The data can describe opinions / sentiments from the public, about products, brands, community services, services, politics, or other topics. Sentiment analysis is one of the fields of Natural Language Processing (NLP) that builds systems for recognizing and extracting opinions in text form. At the most basic level, the goal is to get emotions or 'feelings' from a collection of texts or sentences. The field of sentiment analysis, or also called 'opinion mining', always involves some form of data mining process to get the text that will later be carried out the learning process in the mechine learning that will be built. this study conducts a sentimental analysis with data sources from Twitter using the Random Forest algorithm approach, we will measure the evaluation results of the algorithm we use in this study. The accuracy of measurements in this study, around 75%. the model is good enough. but we suggest trying other algorithms in further research. Keywords: sentiment analysis; random forest algorithm; clasification; machine learnings. 

Hotel recommendation system by bipartite networks and link prediction

2019 ◽  
Vol 46 (1) ◽  
pp. 53-63 ◽  
Author(s):  
Buket Kaya
Keyword(s):  
Social Media ◽  
Link Prediction ◽  
Recommendation System ◽  
Online Reviews ◽  
Bipartite Networks ◽  
Online Social Media ◽  
Negative Comments ◽  
Social Media Tools ◽  
Tourism Enterprises ◽  
The City

With the rapid growth of the Internet in recent years, online social media has become very important for people. People often use social media tools to communicate and share their ideas or experiences regardless of time and place. One of the areas where the use of these tools is widespread is tourism. It is one of the hardest tasks to find a suitable hotel for travellers. There are many websites where accommodation services of the tourism enterprises are evaluated. People share their experiences about the hotels they stayed through these websites. Positive or negative comments are an effective factor in hotel selection. The purpose of this study is to construct a hotel recommendation system based on user’s location using online hotel reviews. The reviews crawled from TripAdvisor.com were filtered according to their total scores, and a dataset was obtained consisting of users and reviews of their liked hotels. For the recommendation task, first, three different bipartite networks consisting of users and hotels were modelled, which were global, country and city based. Then, in these networks, it was recommended hotels to users for their next choice with link prediction methods, using the common hotels where the users prefer to stay in the past. The most successful results were obtained in the city-based network. With this study, it was tried to reduce the time spent reading the online reviews and finding the suitable hotel. To the best of our knowledge, this is the first study on recommending a hotel using online reviews by bipartite networks.

Mining on Social Media

2017 ◽  
pp. 206-222 ◽  
Author(s):  
Ambati Venkata Krishna Prasad ◽  
Venkata Naresh Mandhala
Keyword(s):  
Social Media ◽  
Social Network ◽  
Sentiment Analysis ◽  
Learning Method ◽  
Bayes Classifier ◽  
Social Media Data ◽  
Social Media Mining ◽  
Analysis Technique ◽  
Media Data ◽  
Media Mining

Social media mining is the process of representing, analyzing, and extracting actionable patterns and trends from raw social media data. Social media is favored by many users since it is available to individuals without any limitations to share their opinions, educational learning experiences and concerns via their status. Twitter API, twitter4j, is processed for searching the tweets based on the geo location. Student's posts on social network offers us a stronger concern to take decisions concerning the particular education system's learning method of the system. Evaluating knowledge in social media is sort of a difficult method. Bayes classifier are enforced on deep-mined knowledge for analysis purpose to urge the deeper understanding of the information. It uses multi label classification technique as every label falls into completely different classes. Label based measures are mostly taken to research the results and comparing them with the prevailing sentiment analysis technique.

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