scholarly journals Exploring the sentiment of entrepreneurs on Twitter

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254337
Author(s):  
James Waters ◽  
Nicos Nicolaou ◽  
Dimosthenis Stefanidis ◽  
Hariton Efstathiades ◽  
George Pallis ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Positive Emotions ◽  
Emotional Expressions ◽  
Social Entrepreneurs ◽  
Online Social Media ◽  
Three Samples ◽  
Social Media Platforms ◽  
Serial Entrepreneurs ◽  
Expression Of Emotions

Sentiment analysis is an evolving field of study that employs artificial intelligence techniques to identify the emotions and opinions expressed in a given text. Applying sentiment analysis to study the billions of messages that circulate in popular online social media platforms has raised numerous opportunities for exploring the emotional expressions of their users. In this paper we combine sentiment analysis with natural language processing and topic analysis techniques and conduct two different studies to examine whether engagement in entrepreneurship is associated with more positive emotions expressed on Twitter. In study 1, we investigate three samples with 6.717.308, 13.253.244, and 62.067.509 tweets respectively. We find that entrepreneurs express more positive emotions than non-entrepreneurs for most topics. We also find that social entrepreneurs express more positive emotions, and that serial entrepreneurs express less positive emotions than other entrepreneurs. In study 2, we use 21.491.962 tweets to explore 37.225 job-status changes by individuals who entered or quit entrepreneurship. We find that a job change to entrepreneurship is associated with a shift in the expression of emotions to more positive ones.

scholarly journals Recipe sharing on Pinterest- healthfulness assessment through nutrition information analysis and natural language processing (Preprint)

2020 ◽  
Author(s):  
Xiaolu Cheng ◽  
Shuo-Yu Lin ◽  
Kevin Wang ◽  
Alicia Hong ◽  
Xiaoquan Zhao ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Sugar Content ◽  
Main Ingredient ◽  
Food Ingredients ◽  
Analysis Technique ◽  
Social Media Platforms ◽  
Search History

BACKGROUND Although Pinterest has become a popular platform for distributing influential information that shapes users’ behaviors, the role of recipes pinned on Pinterest has not been well understood. OBJECTIVE To explore patterns of food ingredients and the nutritional content of recipes posted on Pinterest, and examine the factors associated with recipes that engaged more users. METHODS Data were randomly collected from Pinterest between June 28 and July 12, 2020 (207 recipes and 2,818 comments). All samples were collected via two new user accounts with no search history. A codebook was developed with a raw agreement rate of 0.97 across all variables. Content analysis and a novel natural language processing (NLP) sentiment analysis technique were employed. RESULTS Recipes using seafood or vegetables as the main ingredient had on average fewer calories and less sodium, sugar, and cholesterol compared to meat- or poultry-based recipes. For recipes using meat as the main ingredient, more energy was from fat (56.6%). Although the most followed pinners tended to post recipes containing more poultry/seafood and less meat, recipes serving higher fat or providing more calories per serving were more popular, having more shared photos/videos and comments. The NLP-based sentiment analysis suggested that Pinterest users weighted “taste” more heavily than “complexity” (less than 8% of comments) and “health” (less than 3% of comments). CONCLUSIONS While popular pinners tended to post recipes with more seafood/poultry/vegetables and less meat, recipes with higher fat and sugar content were more user-engaging, with more photo/video shares and comments. Data on Pinterest behaviors can inform developing and implementing nutrition health interventions on promoting healthy recipes on social media platforms.

scholarly journals Sentiment Analysis on Twitter Data: A Comparative Approach

2021 ◽  
Vol 9 (10) ◽  
pp. 1-12
Author(s):  
Subhadip Chandra ◽  
Randrita Sarkar ◽  
Sayon Islam ◽  
Soham Nandi ◽  
Avishto Banerjee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Computational Linguistics ◽  
Language Processing ◽  
Research Work ◽  
Comparative Approach ◽  
Support Vector ◽  
Affective States ◽  
Twitter Data ◽  
Social Media Platforms

Sentiment analysis is the methodical recognition, extraction, quantification, and learning of affective states and subjective information using natural language processing, text analysis, computational linguistics, and biometrics. People frequently use Twitter, one of numerous popular social media platforms, to convey their thoughts and opinions about a business, a product, or a service. Analysis of tweet sentiments is particularly useful in detecting if people have a good, negative, or neutral opinion. This study assesses public opinion about an individual, activity, commodity, or organization. The Twitter API is utilised in this article to directly get tweets from Twitter and develop a sentiment categorization for the tweets. This paper has used Twitter data for two separate approaches, viz., Lexicon & Machine Learning. Lexicon based approach further categorized in Corpus-based and Dictionary-based. And various Machine learning-based approaches like Support Vector Machine (SVM), Naïve Bayes, Maximum entropy are used to analyse Twitter data. Neural Network (NN), Decision tree-based sentiment analysis is also covered in this research work, to find out better accuracy of the approaches in the various data range. Graphs and confusion matrices are used to visualise the results of the analysis for positive, negative, and neutral remarks regarding their opinions.

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.

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 MO’ CHARACTERS MO’ PROBLEMS: ONLINE SOCIAL MEDIA PLATFORM CONSTRAINTS AND MODES OF COMMUNICATION

2020 ◽  
Author(s):  
Lewis Mitchell ◽  
Joshua Dent ◽  
Joshua Ross
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Large Scale ◽  
English Language ◽  
Length Change ◽  
Reading Level ◽  
Online Social Media ◽  
Social Media Platforms ◽  
Media Platform ◽  
Processing Techniques

It is widely accepted that different online social media platforms produce different modes of communication, however the ways in which these modalities are shaped by the constraints of a particular platform remain difficult to quantify. On 7 November 2017 Twitter doubled the character limit for users to 280 characters, presenting a unique opportunity to study the response of this population to an exogenous change to the communication medium. Here we analyse a large dataset comprising 387 million English-language tweets (10% of all public tweets) collected over the September 2017--January 2018 period to quantify and explain large-scale changes in individual behaviour and communication patterns precipitated by the character-length change. Using statistical and natural language processing techniques we find that linguistic complexity increased after the change, with individuals writing at a significantly higher reading level. However, we find that some textual properties such as statistical language distribution remain invariant across the change, and are no different to writings in different online media. By fitting a generative mathematical model to the data we find a surprisingly slow response of the Twitter population to this exogenous change, with a substantial number of users taking a number of weeks to adjust to the new medium. In the talk we describe the model and Bayesian parameter estimation techniques used to make these inferences. Furthermore, we argue for mathematical models as an alternative exploratory methodology for "Big" social media datasets, empowering the researcher to make inferences about the human behavioural processes which underlie large-scale patterns and trends.

scholarly journals Spontaneously Generated Online Patient Experience of Modafinil: A Qualitative and NLP Analysis

2021 ◽  
Vol 3 ◽  
Author(s):  
Julia Walsh ◽  
Jonathan Cave ◽  
Frances Griffiths
Keyword(s):  
Side Effects ◽  
Patient Experience ◽  
Language Processing ◽  
Analysis Data ◽  
Current Evidence ◽  
Term Extraction ◽  
Online Social Media ◽  
Wide Range ◽  
Social Media Platforms ◽  
N Gram

Objective: To compare the findings from a qualitative and a natural language processing (NLP) based analysis of online patient experience posts on patient experience of the effectiveness and impact of the drug Modafinil.Methods: Posts (n = 260) from 5 online social media platforms where posts were publicly available formed the dataset/corpus. Three platforms asked posters to give a numerical rating of Modafinil. Thematic analysis: data was coded and themes generated. Data were categorized into PreModafinil, Acquisition, Dosage, and PostModafinil and compared to identify each poster's own view of whether taking Modafinil was linked to an identifiable outcome. We classified this as positive, mixed, negative, or neutral and compared this with numerical ratings. NLP: Corpus text was speech tagged and keywords and key terms extracted. We identified the following entities: drug names, condition names, symptoms, actions, and side-effects. We searched for simple relationships, collocations, and co-occurrences of entities. To identify causal text, we split the corpus into PreModafinil and PostModafinil and used n-gram analysis. To evaluate sentiment, we calculated the polarity of each post between −1 (negative) and +1 (positive). NLP results were mapped to qualitative results.Results: Posters had used Modafinil for 33 different primary conditions. Eight themes were identified: the reason for taking (condition or symptom), impact of symptoms, acquisition, dosage, side effects, other interventions tried or compared to, effectiveness of Modafinil, and quality of life outcomes. Posters reported perceived effectiveness as follows: 68% positive, 12% mixed, 18% negative. Our classification was consistent with poster ratings. Of the most frequent 100 keywords/keyterms identified by term extraction 88/100 keywords and 84/100 keyterms mapped directly to the eight themes. Seven keyterms indicated negation and temporal states. Sentiment was as follows 72% positive sentiment 4% neutral 24% negative. Matching of sentiment between the qualitative and NLP methods was accurate in 64.2% of posts. If we allow for one category difference matching was accurate in 85% of posts.Conclusions: User generated patient experience is a rich resource for evaluating real world effectiveness, understanding patient perspectives, and identifying research gaps. Both methods successfully identified the entities and topics contained in the posts. In contrast to current evidence, posters with a wide range of other conditions found Modafinil effective. Perceived causality and effectiveness were identified by both methods demonstrating the potential to augment existing knowledge.

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. 

scholarly journals Perspectives of the performance metrics in lexicon and hybrid based approaches: a review

2017 ◽  
Vol 6 (4) ◽  
pp. 108 ◽  
Author(s):  
Meesala Shobha Rani ◽  
Sumathy S
Keyword(s):  
Text Mining ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Performance Metrics ◽  
Pearson Correlation ◽  
Mining Machine ◽  
Broad Perspective ◽  
Interdisciplinary Field ◽  
Online Social Media ◽  
Active Research

Online social media and social networking services experience a drastic development in the present scenario. Contents generated by hundreds of millions of users are used for communication in general. Users mark their opinion and review in various applications such as Twitter, Facebook, YouTube, Weibo, Flicker, LinkedIn, Online-e commerce sites, Microblogging sites, etc. User generated text is spread rapidly on the web, and it has become tedious to analyze the opinionated text in order to arrive at a decision. Sentiment analysis, a sub-category of text mining is the major active research domain in current era due to greater quantity of opinionated text present in the Internet. Semantic detection is the sub-class in the sentiment analysis which is used for measuring the sentiment orientation in any text. Opinionated text is used for analyzing and making the decision simple. This interdisciplinary field draws various techniques from data mining, machine learning, natural language processing, lexicon based and hybrid based approaches. This paper provides a broad perspective with the highlight of the current state-of art techniques emphasizing the various research challenges and gaps present. The performance metrics in terms of detection rate, precision, recall, f-measure/score, average mean, auto-Pearson correlation, cosine similarity and ratio of time on various algorithms is discussed in detail. An analysis of the text mining approaches in different domains is presented.

A Hybrid CNN-LSTM: A Deep Learning Approach for Consumer Sentiment Analysis Using Qualitative User-Generated Contents

2021 ◽  
Vol 20 (5) ◽  
pp. 1-15
Author(s):  
Praphula Kumar Jain ◽  
Vijayalakshmi Saravanan ◽  
Rajendra Pamula
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Online Reviews ◽  
Web Content ◽  
Proposed Model ◽  
Social Media Platforms ◽  
Information And Communication ◽  
Better Than

With the fastest growth of information and communication technology (ICT), the availability of web content on social media platforms is increasing day by day. Sentiment analysis from online reviews drawing researchers’ attention from various organizations such as academics, government, and private industries. Sentiment analysis has been a hot research topic in Machine Learning (ML) and Natural Language Processing (NLP). Currently, Deep Learning (DL) techniques are implemented in sentiment analysis to get excellent results. This study proposed a hybrid convolutional neural network-long short-term memory (CNN-LSTM) model for sentiment analysis. Our proposed model is being applied with dropout, max pooling, and batch normalization to get results. Experimental analysis carried out on Airlinequality and Twitter airline sentiment datasets. We employed the Keras word embedding approach, which converts texts into vectors of numeric values, where similar words have small vector distances between them. We calculated various parameters, such as accuracy, precision, recall, and F1-measure, to measure the model’s performance. These parameters for the proposed model are better than the classical ML models in sentiment analysis. Our results analysis demonstrates that the proposed model outperforms with 91.3% accuracy in sentiment analysis.

scholarly journals SENTIMENT ANALYSIS FOR SOCIAL MEDIA: A SURVEY

2021 ◽  
Vol 37 (4) ◽  
pp. 403-428
Author(s):  
Huyen Trang Phan ◽  
Ngoc Thanh Nguyen ◽  
Dosam Hwang
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Rapid Development ◽  
General Information ◽  
Future Research ◽  
Research Directions ◽  
Internet Industry ◽  
Social Media Platforms ◽  
Data Source

With the rapid development of the Internet industry, an increasing number of social media platforms have been developed. These social media platforms have become the main channels for communication among most users. Opinions from social media platforms provide the most updated and inclusive information. Sentiments from opinions are a valuable data source for solving many issues. Therefore, sentiment analysis has developed into one of the most popular natural language processing fields. Hence, improving the performance of sentiment analysis methods or discovering new problems related to these methods is essential. In this context, we must be aware of the general information relevant to this area. This survey presents a summary of the necessary stages for building a complete model to be used in sentiment analysis. For each procedure, we list the popular techniques that have been widely used in recent years. In addition, discussions and comparisons related to these methods are provided. Additionally, we discuss the challenges and possible research directions for future research in this field.

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