scholarly journals A Sentiment Analysis Tool for Determining the Promotional Success of Fashion Images on Instagram

2017 ◽  
Vol 11 (2) ◽  
pp. 66 ◽  
Author(s):  
Mohamed AbdelFattah ◽  
Dahab Galal ◽  
Nada Hassan ◽  
Doaa Elzanfaly ◽  
Greg Tallent
Keyword(s):  
Social Media ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Social Value ◽  
High Rate ◽  
Basic Question ◽  
Analysis Tool ◽  
Visual Aesthetics ◽  
The Social

<p>Sentiment Analysis (SA) or Opinion Mining is the process of analysing natural language texts to detect an emotion or a pattern of emotions towards a certain product to make a decision about that product. SA is a topic of text mining, Natural Language Processing (NLP) and web mining disciplines. Research in SA is currently at its peak given the amount of data generated from social media networks. The concept is that consumers are expressing exactly what they need, want and expect from a product but on the other hand the companies don’t have the tools to analyse and understand these feelings to satisfy these consumers accordingly. </p><p>One of the applications that generate a high rate of reactions and sentiments in social networks is Instagram. This study focuses on analysing the reactions generated by the top 50 fashion houses on Instagram given their top 20 images with the highest number of likes. The approach taken in this study is to qualify the visual aesthetics of fashion images and to establish why some succeed on social media more than others. </p><p class="Els-Abstract-text">The basic question asked in this paper is whether there are certain visual aesthetics that appeal more to the user and are therefore more successful on social media than others as determined by a measure we introduce, ‘Social Value’. To do so, a sentiment analysis tool is developed to measure the proposed social value of each image. An input of comments from each image will be processed. Each comment will go through a pre-processing phase; each word will be placed through a lexicon to identify if it is positive or negative. The output of the lexicon is a score value assigned to each comment to identify its degree of positivity, negativity, or it has no effect on the social value. Adding to these results, the number of likes and shares would also be taken into consideration quantifying the image’s value. A cumulative result is then produced to determine the social value of an image.</p>

scholarly journals Natural language processing for analysis of student online sentiment in a postgraduate program

2020 ◽  
Vol 2 (2) ◽  
pp. 15-30
Author(s):  
Truc D Pham ◽  
Darcy Vo ◽  
Frank Li ◽  
Karen Baker ◽  
Binglan Han ◽  
...  
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Experience ◽  
Research Question ◽  
The Social ◽  
Natural Language Processing Tool ◽  
Course Experience

Higher education institutes are continually looking for new and better ways to support and understand the learning experience of their students. One possible option is to use sentiment analysis tools to investigate the attitudes and emotions of students when they are interacting on social media about their course experience. In this study, we analysed the social media posts, from a closed programme-based community, of more than 300 students in a single programme cohort by processing the dataset with the Google cloud-based Natural Language Processing API for sentiment analysis. The sentiment scores and magnitudes were then visualised to help explore the research question ‘How does a natural language processing tool help analyse student online sentiment in a postgraduate program?’ The results have provided a better understanding of students’ online sentiment relating to the activities and assessments of the programme as well as the variation of that sentiment over the timeline of the programme.

Deep Learning Techniques for Polarity Classification in Multimodal Sentiment Analysis

2018 ◽  
Vol 17 (03) ◽  
pp. 883-910 ◽  
Author(s):  
P. D. Mahendhiran ◽  
S. Kannimuthu
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Relevant Information ◽  
Neural Net ◽  
Video Information ◽  
Automatic Feature Extraction ◽  
Multimodal Sentiment Analysis

Contemporary research in Multimodal Sentiment Analysis (MSA) using deep learning is becoming popular in Natural Language Processing. Enormous amount of data are obtainable from social media such as Facebook, WhatsApp, YouTube, Twitter and microblogs every day. In order to deal with these large multimodal data, it is difficult to identify the relevant information from social media websites. Hence, there is a need to improve an intellectual MSA. Here, Deep Learning is used to improve the understanding and performance of MSA better. Deep Learning delivers automatic feature extraction and supports to achieve the best performance to enhance the combined model that integrates Linguistic, Acoustic and Video information extraction method. This paper focuses on the various techniques used for classifying the given portion of natural language text, audio and video according to the thoughts, feelings or opinions expressed in it, i.e., whether the general attitude is Neutral, Positive or Negative. From the results, it is perceived that Deep Learning classification algorithm gives better results compared to other machine learning classifiers such as KNN, Naive Bayes, Random Forest, Random Tree and Neural Net model. The proposed MSA in deep learning is to identify sentiment in web videos which conduct the poof-of-concept experiments that proved, in preliminary experiments using the ICT-YouTube dataset, our proposed multimodal system achieves an accuracy of 96.07%.

scholarly journals Application of Naïve Bayes Algorithm in Sentiment Analysis of Filipino, English and Taglish Facebook Comments

2020 ◽  
Vol 4 (5) ◽  
pp. 73-77
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Product Reviews ◽  
Documentary Data ◽  
The Social ◽  
Bayes Algorithm

The World Wide Web has boosted its content for the past years, it has a vast amount of multimedia resources that continuously grow specifically in documentary data. One of the major contributors of documentary contents can be evidently found on the social media called Facebook. People or netizens on Facebook are actively sharing their opinion about a certain topic or posts that can be related to them or not. With the huge amount of accessible documentary data that are seen on the so-called social media, there are research trends that can be made by the researchers in the field of opinion mining. A netizen’s comment on a particular post can either be a negative or a positive one. This study will discuss the opinion or comment of a netizen whether it is positive or negative or how she/he feels about a specific topic posted on Facebook; this is can be measured by the use of Sentiment Analysis. The combination of the Natural Language Processing and the analytics in textual form is also known as Sentiment Analysis that is use to the extraction of data in a useful manner. This study will be based on the product reviews of Filipinos in Filipino, English and Taglish (mixed Filipino and English) languages. To categorize a comment effectively, the Naïve Bayes Algorithm was implemented to the developed web system.

scholarly journals In Silico Method for Sentiment and Emotion Analysis in Medicine Using Social Media (Preprint)

2020 ◽  
Author(s):  
Anne Xuan-Lan Nguyen ◽  
Xuan-Vi Trinh ◽  
Sophia Y. Wang ◽  
Albert Y. Wu
Keyword(s):  
Social Media ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Free Text ◽  
Emotion Analysis ◽  
Search Terms ◽  
Patient Values ◽  
Ibm Watson ◽  
Medical Subspecialty

BACKGROUND Clinical data present in social media is an underused source of information with great potential to allow for a deeper understanding of patient values, attitudes and preferences. OBJECTIVE We describe a novel and broadly applicable method for sentiment analysis and emotion detection to free text from online medical health forums and the factors to consider during its application. METHODS We mined the full discussion and user information of all posts containing search terms related to a specific medical subspecialty (oculoplastics) from MedHelp, the largest online platform for patient health forums. We employed a variety of data cleaning and processing to define the relevant subset of results and prepare those results for sentiment analysis. We executed sentiment and emotion analysis through IBM Watson Natural Language Understanding service to generate sentiment and emotion scores for the posts and their associated keywords. Keywords were aggregated using natural language processing tools. RESULTS 39 oculoplastics-related search terms resulted in 46,381 eligible posts within 14,329 threads, written by 18,319 users (117 doctors; 18,202 patients) and 201,611 associated keywords. Keywords that occurred ≥500 times in the corpus were used to identify most prominent topics, including specific symptoms, medication and complications. The sentiment and emotion scores of these keywords and eligible posts were further analyzed to provide concrete examples of the methodology’s potential to allow better understanding of patients’ attitudes. CONCLUSIONS This comprehensive report allows physicians and researchers to efficiently mine and perform sentiment analysis on social media to better understand patients’ perspectives and promote patient-centric care. Important factors to be considered during application include evaluating the scope of the search, selecting search terms and understanding their different linguistic usages, and establishing robust selection, filtering and processing criteria for posts and keywords tailored to the results.

scholarly journals An Exploration of Impact of COVID 19 on mental health -Analysis of tweets using Natural Language Processing techniques

2020 ◽  
Author(s):  
Sohini Sengupta ◽  
Sareeta Mugde ◽  
Garima Sharma
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Gold Mine ◽  
Social Media Platforms ◽  
Processing Techniques

Twitter is one of the world's biggest social media platforms for hosting abundant number of user-generated posts. It is considered as a gold mine of data. Majority of the tweets are public and thereby pullable unlike other social media platforms. In this paper we are analyzing the topics related to mental health that are recently (June, 2020) been discussed on Twitter. Also amidst the on-going pandemic, we are going to find out if covid-19 emerges as one of the factors impacting mental health. Further we are going to do an overall sentiment analysis to better understand the emotions of users.

scholarly journals Twitter Sentiment Analysis Model

2021 ◽  
Vol 9 (VII) ◽  
pp. 296-302
Author(s):  
Pushkar Dubey
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Approach ◽  
Analysis Model ◽  
Market Place ◽  
The Social

Social networks are the main resources to gather information about people’s opinion towards different topics as they spend hours daily on social media and share their opinion. Twitter is one of the social media that is gaining popularity. Twitter offers organizations a fast and effective way to analyze customers’ perspectives toward the critical to success in the market place. Developing a program for sentiment analysis is an approach to be used to computationally measure customers’ perceptions. .We use natural language processing and machine learning concepts to create a model for analysis . In this paper we are discussing how we can create a model for analysis of twittes which is trained by various nlp , machine learning and Deep learning Approach.

Coronavirus Pandemic (COVID-19)

2021 ◽  
Vol 17 (2) ◽  
pp. 1-21
Author(s):  
Jalal S. Alowibdi ◽  
Abdulrahman A. Alshdadi ◽  
Ali Daud ◽  
Mohamed M. Dessouky ◽  
Essa Ali Alhazmi
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Experimental Results ◽  
Mixed Emotions ◽  
Social Media Platforms

People are afraid about COVID-19 and are actively talking about it on social media platforms such as Twitter. People are showing their emotions openly in their tweets on Twitter. It's very important to perform sentiment analysis on these tweets for finding COVID-19's impact on people's lives. Natural language processing, textual processing, computational linguists, and biometrics are applied to perform sentiment analysis to identify and extract the emotions. In this work, sentiment analysis is carried out on a large Twitter dataset of English tweets. Ten emotional themes are investigated. Experimental results show that COVID-19 has spread fear/anxiety, gratitude, happiness and hope, and other mixed emotions among people for different reasons. Specifically, it is observed that positive news from top officials like Trump of chloroquine as cure to COVID-19 has suddenly lowered fear in sentiment, and happiness, gratitude, and hope started to rise. But, once FDA said, chloroquine is not effective cure, fear again started to rise.

scholarly journals Sentiment Analysis for Depression Based on Social Media Post by Using Natural Language Processing

2021 ◽  
pp. 134-139
Author(s):  
G. Neelavathi ◽  
D. Sowmiya ◽  
C. Sharmila ◽  
J. Vaishnavi
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Rich Source ◽  
Research Center ◽  
Depression Symptoms ◽  
Linguistic Markers

Presently Research Center expresses that, 72% of public uses some sort of social media. More than 300 million individual experiences the depression and despondency, just a small amount of them get sufficient treatment. Discouragement is the main source of incapacity worldwide and almost 800,000 individuals consistently loss their life because of suicide. Suicide is the subsequent driving reason for death among teenagers. Our idea is to suggest solution for this problem. Social Media gives an extraordinary chance to change early depressions, especially in youngsters. Consistently, around 6,000 Tweets are tweeted per second, 350,000 tweets per minute, 500 million tweets each day and around 200 billion tweets each year. By using this rich source of data and information, can efficient model which provides report of person’s depression symptoms will be designed. In this model an algorithm that can examine Tweets Expressing self-assessed negative features by analyzing linguistic markers in social media posts.

scholarly journals Low Testosterone on Social Media: Application of Natural Language Processing to Understand Patients’ Perceptions of Hypogonadism and Its Treatment

10.2196/21383 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e21383
Author(s):  
Vadim Osadchiy ◽  
Tommy Jiang ◽  
Jesse Nelson Mills ◽  
Sriram Venkata Eleswarapu
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Principal Component ◽  
Online Discussions ◽  
Testosterone Replacement ◽  
Testosterone Replacement Therapy ◽  
The Social ◽  
Low Testosterone

Background Despite the results of the Testosterone Trials, physicians remain uncomfortable treating men with hypogonadism. Discouraged, men increasingly turn to social media to discuss medical concerns. Objective The goal of the research was to apply natural language processing (NLP) techniques to social media posts for identification of themes of discussion regarding low testosterone and testosterone replacement therapy (TRT) in order to inform how physicians may better evaluate and counsel patients. Methods We retrospectively extracted posts from the Reddit community r/Testosterone from December 2015 through May 2019. We applied an NLP technique called the meaning extraction method with principal component analysis (MEM/PCA) to computationally derive discussion themes. We then performed a prospective analysis of Twitter data (tweets) that contained the terms low testosterone, low T, and testosterone replacement from June through September 2019. Results A total of 199,335 Reddit posts and 6659 tweets were analyzed. MEM/PCA revealed dominant themes of discussion: symptoms of hypogonadism, seeing a doctor, results of laboratory tests, derogatory comments and insults, TRT medications, and cardiovascular risk. More than 25% of Reddit posts contained the term doctor, and more than 5% urologist. Conclusions This study represents the first NLP evaluation of the social media landscape surrounding hypogonadism and TRT. Although physicians traditionally limit their practices to within their clinic walls, the ubiquity of social media demands that physicians understand what patients discuss online. Physicians may do well to bring up online discussions during clinic consultations for low testosterone to pull back the curtain and dispel myths.

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