Positive Sentiment Score Research Articles

Predicting Election Results from Twitter Using Machine Learning Algorithms

Introduction: In the present scenario, the social media network plays a significant role in sharing information between individuals. This incorporates information about news and events that are presently occurring in the worldwide. Anticipating election results is presently turning into a fascinating research topic through social media. In this article, we proposed a strategy to anticipate election results by consolidating sub-event discovery and sentimental analysis in micro-blogs to break down as well as imagine political inclinations uncovered by those social media users. Methods: This approach discovers and investigates sentimental data from micro-blogs to anticipate the popularity of contestants. In general, many organizations and media houses conduct a pre-poll review and obtain expert’s perspectives to anticipate the result of the election, but for our model, we use Twitter data to predict the result of an election by gathering information from Twitter and evaluate it to anticipate the result of the election by analyzing the sentiment of twitter information about the contestants. Results: The number of seats won by the first, second, and third party in AP Assembly Election 2019 has been determined by utilizing Positive Sentiment Scores (PSS’s) of the parties. The actual results of the election and our predicted values of the proposed model are compared, and the outcomes are very close to actual results. We utilized machine learning-based sentimental analysis to discover user emotions in tweets, anticipate sentiment score, and then convert this sentiment score to parties' seat score. Comprehensive experiments are conducted to check out the performance of our model based on a Twitter dataset. Conclusions: Our outcomes state that the proposed model can precisely forecast the election results with accuracy (94.2 %) over the given baselines. The experimental outcomes are very closer to actual election results and contrasted with conventional strategies utilized by various survey agencies for exit polls and approval of results demonstrated that social media data can foresee with better exactness. Discussion: In the future, we might want to expand this work into different areas and nations of the reality where Twitter is picking up prevalence as a political battling tool, and where politicians and individuals are turning towards micro-blogs for political communication and data. We would likewise expand this research into various fields other than general elections and from politicians to state organizations.

Sentiment Analysis Technique and Neutrosophic Set Theory for Mining and Ranking Big Data From Online Reviews

Recently, a huge amount of online consumer reviews (OCRs) is being generated through social media, web contents, and microblogs. This scale of big data cannot be handled by traditional methods. Sentiment analysis (SA) or opinion mining is emerging as a powerful and efficient tool in big data analytics and improving decision making. This research paper introduces a novel method that integrates neutrosophic set (NS) theory into the SA technique and multi-attribute decision making (MADM) to rank the different products based on numerous online reviews. The method consists of two parts: Determining sentiment scores of the online reviews based on the SA technique and ranking alternative products via NS theory. In the first part, the online reviews of the alternative products concerning multiple features are crawled and pre-processed. A neutral lexicon consists of 228 neutral words and phrases is compiled and the Valence Aware Dictionary and sEntiment Reasoner (VADER) for sentiment reasoning is adapted to handle the neutral data. The compiled neutral lexicon, as well as the adapted VADER, are utilized to build a novel adaptation called Neutro-VADER. The Neutro-VADER assigns positive, neutral, and negative sentiment scores to each review concerning the product feature. In this stage, the novel idea is to point out the positive, neutral, and negative sentiment scores as the truth, indeterminacy, and falsity memberships degrees of the neutrosophic number. The overall performance of each alternative concerning each feature based on a neutrosophic number is measured. In the second part, the ranking of alternatives is being evaluated through the simplified neutrosophic number weighted averaging (SNNWA) operator and cosine similarity measure methods. A case study with real datasets (Twitter datasets) is provided to illustrate the application of the proposed method. The results show good performance in handling the neutral data on the SA stage as well as the ranking stage. In the SA stage, findings show that the Neutro-VADER in the proposed method can deal successfully with all types of uncertainties including indeterminacy comparable with the traditional VADER in the other methods. In the ranking stage, the results show a great similarity and consistency while using other ranking methods such as PROMETHEE II, TOPSIS, and TODIM methods.

Flipped Classroom in Podiatric Medical Education.

The purpose of this study was to evaluate student learning outcomes in a flipped classroom versus a traditional classroom in a podiatric medical school. To date no published reports in podiatric medical schools have used the flipped classroom for the entirety of a medical school course. Students from the class of 2017 completed the Emergency Medicine and Trauma course using traditional classroom lectures, and the class of 2018 used a flipped classroom approach. Each class took two assessments that contained 99 identical questions and completed a postcourse evaluation that contained student comments. A multivariate analysis of covariance was conducted to determine whether student performances were significantly affected by the differences in the teaching method. Student evaluation comments were analyzed using textual data analysis to determine the sentiments that students expressed regarding their exposure to the teaching method. The multivariate analysis of covariance results revealed that students scored slightly lower on assessments during the flipped classroom delivery compared with the traditional classroom delivery, when adjusted for Medical College Admission Test scores and grade point average, but not significantly (P = .4340). Similarly, the sentiment analysis of student comments indicated that the average positive sentiment score for the flipped classroom delivery was higher but was not significant (P = .08914). The analysis showed there was not a statistically significant change in examination scores based on teaching method. Sentiment analysis revealed that student sentiments were more positive with the flipped classroom group compared with the traditional lecture group, although not statistically significantly.

A Social Media Study on the Associations of Flavored Electronic Cigarettes With Health Symptoms: Observational Study.

BackgroundIn recent years, flavored electronic cigarettes (e-cigarettes) have become popular among teenagers and young adults. Discussions about e-cigarettes and e-cigarette use (vaping) experiences are prevalent online, making social media an ideal resource for understanding the health risks associated with e-cigarette flavors from the users’ perspective.ObjectiveThis study aimed to investigate the potential associations between electronic cigarette liquid (e-liquid) flavors and the reporting of health symptoms using social media data.MethodsA dataset consisting of 2.8 million e-cigarette–related posts was collected using keyword filtering from Reddit, a social media platform, from January 2013 to April 2019. Temporal analysis for nine major health symptom categories was used to understand the trend of public concerns related to e-cigarettes. Sentiment analysis was conducted to obtain the proportions of positive and negative sentiment scores for all reported health symptom categories. Topic modeling was applied to reveal the topics related to e-cigarettes and health symptoms. Furthermore, generalized estimating equation (GEE) models were used to quantitatively measure potential associations between e-liquid flavors and the reporting of health symptoms.ResultsTemporal analysis showed that the Respiratory category was consistently the most discussed health symptom category among all categories related to e-cigarettes on Reddit, followed by the Throat category. Sentiment analysis showed higher proportions of positive sentiment scores for all reported health symptom categories, except for the Cancer category. Topic modeling conducted on all health-related posts showed that 17 of the top 100 topics were flavor related. GEE models showed different associations between the reporting of health symptoms and e-liquid flavor categories, for example, lower association of the Beverage flavors with Respiratory compared with other flavors and higher association of the Fruit flavors with Cardiovascular than other flavors.ConclusionsThis study identified different potential associations between e-liquid flavors and the reporting of health symptoms using social media data. The results of this study provide valuable information for further investigation of the health effects associated with different e-liquid flavors.

Associations Between Affective States and Sexual and Health Status Among Men Who Have Sex With Men in China: Exploratory Study Using Social Media Data.

BackgroundAffective states, including sentiment and emotion, are critical determinants of health. However, few studies among men who have sex with men (MSM) have examined sentiment and emotion specifically using real-time social media technologies. Moreover, the explorations on their associations with sexual and health status among MSM are limited.ObjectiveThis study aimed to understand and examine the associations of affective states with sexual behaviors and health status among MSM using public data from the Blued (Blued International Inc) app.MethodsA total of 843,745 public postings of 377,610 MSM users located in Guangdong were saved from the Blued app by automatic screen capture. Positive affect, negative affect, sexual behaviors, and health status were measured using the Simplified Chinese Linguistic Inquiry and Word Count. Emotions, including joy, sadness, anger, fear, and disgust, were measured using the Weibo Basic Mood Lexicon. A positive sentiment score and a positive emotion score were also calculated. Univariate and multivariate linear regression models on the basis of a permutation test were used to assess the associations of affective states with sexual behaviors and health status.ResultsA total of 5871 active MSM users and their 477,374 postings were finally selected. Both positive affect and positive emotions (eg, joy) peaked between 7 AM and 9 AM. Negative affect and negative emotions (eg, sadness and disgust) peaked between 2 AM and 4 AM. During that time, 25.1% (97/387) of negative postings were related to health and 13.4% (52/387) of negative postings were related to seeking social support. A multivariate analysis showed that the MSM who were more likely to post sexual behaviors were more likely to express positive affect (beta=0.3107; P<.001) and positive emotions (joy: beta=0.027; P<.001), as well as negative emotions (sadness: beta=0.0443; P<.001 and disgust: beta=0.0256; P<.001). They also had a higher positive sentiment score (beta=0.2947; P<.001) and a higher positive emotion score (beta=0.1612; P<.001). The MSM who were more likely to post their health status were more likely to express negative affect (beta=0.8088; P<.001) and negative emotions, including sadness (beta=0.0705; P<.001), anger (beta=0.0058; P<.001), fear (beta=0.0052; P<.001), and disgust (beta=0.3065; P<.001), and less likely to express positive affect (beta=−0.0224; P=.02). In addition, they had a lower positive sentiment score (beta=−0.8306; P<.001) and a lower positive emotion score (beta=−0.3743; P<.001).ConclusionsThe MSM social media community mainly expressed their positive affect in the early morning and negative affect after midnight. Positive affective states were associated with being sexually active, whereas negative affective states were associated with health problems, mostly about mental health. Our finding suggests the potential to deliver different health-related intervention strategies (eg, psychological counseling and safe sex promotion) on a social media app according to the affective states of MSM in real time.

Analyzing the Sentiment of MOOC Discussion Posts

The purpose of the project is to identify common difficulties that learners may face and to understand their emotions as they progress through MOOCs. MOOC is an abbreviation for the Massive Open Online Course and the research deals with the data from ten different courses from Coursera. The data is used to extract pieces of text that students have made. Then, those certain texts are required to be sent to Google Cloud Natural Language API. This app allows users to get a sentiment analysis of a text. The main goal is to assist instructors with monitoring MOOC to make it more efficient and easier for students to progress since it assists to improve the courses.&#x0D; To achieve this, the first step is to gather all the data from each of the courses. Then use programming to dump all that data into one big database. The program that is used here is called Pycharm and user is required to use python and sql to aid him in dumping the data in the database. Once the database is created, coding is done to only select out the pieces of information that are needed. These texts should be where students make comments or ask questions. Next, the data is queried to send these texts to Google Cloud Natural Language API. Here, the program breaks down all the sentences to only be just words. Then the program is going to categorize each word according to whether its connotation is positive, negative or neutral. Next, all the words are sorted according to their connotations. The overall sentiment depends on the emotion that has the highest number. If positives and negatives are all balanced out then the sentiment is neutral. Sentiment scores range from -1 to 1, where -1 is the most negative, 1 is the most positive and anywhere near 0 is neutral.&#x0D; Positive sentiment scores indicate instructors that students are doing well on their course and neutral sentiment scores indicate that the course is balanced out with difficulties and easy tasks. However, negative sentiment is the most important to instructors since it indicates them that students are struggling and they need to improve the course.

P021 EMOTIONAL TOLL OF INFLAMMATORY BOWEL DISEASE: LESSONS FROM THE THEMATIC ANALYSIS OF ONLINE COMMUNITY FORUMS

Patients with inflammatory bowel disease (IBD) are increasingly turning to online community forums (OCFs) to seek and share information, yet little is known about their userbase or content, which may dissuade providers from referring patients to OCFs. OCFs offer insight into patient priorities and their psychosocial well-being. We used topic modeling and sentiment analysis to examine sources of emotional distress and coping among IBD patients and their supporters on the largest IBD OCF, the Crohn’s & Colitis Foundation Community Forum. We analyzed 51,591 online posts and demographic data from 28,623 users from the Crohn’s & Colitis Foundation Community Forum generated from 1/22/08 to 11/17/17. We extracted self-reported demographic information and post content. We used ATLAS.ti® inductive analysis software to manually identify key themes, including emotions, in the 100 most popular posts of 2008, 2012, and 2016. Nine major emotion themes were observed and a word list of synonyms for each emotion was created. We used the word lists to extract all posts expressing each emotion theme and used latent Dirichlet allocation to perform thematic content analysis. We performed sentiment analysis using the TextBlog library for Python™. Sentiment is scored from -1, the most negative sentiment, to +1, the most positive sentiment. Among all registered users on the Crohn’s & Colitis Foundation Community Forum, 10,704 (34.4%) contributed at least one post and a subset of 1,455 (5.1%) users were at the 95th percentile of posting activity. All posts generated 2,541,240 views. Compared to less active users (who contributed at least one post), active users were younger (36.6 vs 38.4, p<0.0001), more likely to be female (78% vs 73.5%, p=0.0006), definitively diagnosed with IBD (92.9% vs 88%, p<0.0001), and diagnosed more recently (5.7 years vs 7.6, p<0.0001) (Table 1). Thematic analysis revealed 10,720 posts expressing at least one of 9 major emotion categories: Gratitude (40.0%), anxiety/fear (20.8%), empathy (18.2%), frustration/anger (13.4%), hope (13.2%), happiness (10.0%), depression/sadness (5.8%), shame/guilt (2.5%), and loneliness (2.5%). We identified the top three topics for each emotion (Figure 1). Posts expressing happiness, gratitude, or hope comprised the majority and were associated with more positive mean sentiment scores (0.33, 0.28, 0.12, respectively) compared to all posts on the forum (0.10, p<0.001). The majority of posts conveying an emotion on the Crohn’s & Colitis Foundation Community Forum were associated with positive sentiment. Posts expressing happiness, gratitude, or hope tended to convey positive messages on the importance of social support and gratitude towards other users in the online community. OCFs are an outlet for those affected by IBD to seek and provide advice in a supportive environment.Table 1Crohn’s & Colitis Foundation Community Forum user demographics from 2008 to 2017 Open table in a new tab

Web data geostatistics and analytics to evaluate the impact of a cultural event

This work proposes a methodological approach that analyses and interprets data resulting from cultural events fruition, in order to obtain information useful to improve cultural events management and encourage the cooperation between the different stakeholders involved. The methodology, based on geostatistics and text analytics techniques, has been proposed and applied to a real case study: the Italian folk music festival 'La Notte della Taranta'. In particular, text analytics techniques, like semantic and sentiment analysis, integrated with spatial analysis techniques, allowed identifying sentiment score spatial variation and locating geographical areas characterised by negative or positive average sentiment score, in order to understand the impact of a cultural event on the local territory. The particular structure of the event (itinerant) and the Salento territory where it is rooted, an area rich in traditions and folklore, make this festival an appropriate use case for the approach proposed.

\u201cMy Invisalign experience\u201d: content, metrics and comment sentiment analysis of the most popular patient testimonials on YouTube

BackgroundThe aim of the study was to investigate the popularity, content of Invisalign patient testimonials on YouTube, as well as the sentiment of the related comments.MethodsUsing the term “Invisalign experience,” the top 100 results on YouTube by view count were screened for English spoken patient videos that attracted comments. Video information (time since video upload, sponsorship), engagement metrics (comments, likes, dislikes, subscriptions), and views were collected. Videos were rated for information completeness (ICS), and comments were classified by origin and content. The emotional loading of the comments was measured using automated sentiment analysis.ResultsThe 40 reviewed testimonials scored an average ICS of 3.78 (SD 0.97). ICS, time since upload, and video duration did not appear to significantly influence the number of views, subscriptions, likes, dislikes, and comments. There was a statistically significant difference (P = 0.03) between mean positive (2.01, SD 0.95) and negative sentiment scores (− 1.90, SD 1.14). Commenter’s status and overall comment on video were significantly associated with positive sentiment scores. There was a significant association between sponsorship, commenter’s status, overall comment on video, focus of concern, perceived Invisalign’s disadvantages, and increased negative sentiment scores.ConclusionsEngagement of audience and views of the most popular Invisalign patient testimonials were not significantly influenced by completeness of information, video duration, and lifespan. The sentiment of viewers’ comments about Invisalign treatment was significantly more positive and was significantly associated with their status, content, and sponsorship of videos. Orthodontic trends on YouTube need to be cautiously monitored for planning interventions that improve patients’ knowledge about orthodontics.

Fear, Greed and Efficient Market- Evidence from News Sentiment Analytics

I analyze the relationship between natural gas prices and the news sentiment data obtained from the Thomson Reuters News Analytics system. I conduct studies on two different horizons: the daily basis study and the intraday basis study. For the daily study, our results show that any correlation we observed between the daily news sentiments and the daily returns of natural gas can be largely attributed to the news relevant to historical natural gas price movements, or “price-related news”. This discovery also emphasizes the failure of price-related news sentiment in predicting future natural gas returns, which in turn supports the weak form of the Efficient Market Hypothesis. For the intraday study, I label news items with high positive and negative sentiment scores as extreme positive news and extreme negative news. I then conduct event study to analyze the price-related extreme new items’ impact on natural gas prices. I found natural gas has two different types of price movement in reaction to price-related extreme negative news. I also show that these two types of abnormal return can serve as a signal for the investors’ sentiment. I made a connection between these investors’ sentiment signals and the S&P 500 index to show that these signals can be used to predict long term S&P 500 returns. The capability of these signals to predict the future S&P 500 index and the fact that the signals are derived solely from past price reactions and past price-related news items suggests that the current natural gas prices and equity market prices do not fully reflect all the information related to historical prices.

A sentiment analysis of U.S. local government tweets: The connection between tone and citizen involvement

As social media tools become more popular at all levels of government, more research is needed to determine how the platforms can be used to create meaningful citizen–government collaboration. Many entities use the tools in one-way, push manners. The aim of this research is to determine if sentiment (tone) can positively influence citizen participation with government via social media. Using a systematic random sample of 125 U.S. cities, we found that positive sentiment is more likely to engender digital participation but this was not a perfect one-to-one relationship. Some cities that had an overall positive sentiment score and displayed a participatory style of social media use did not have positive citizen sentiment scores. We argue that positive tone is only one part of a successful social media interaction plan, and encourage social media managers to actively manage platforms to use activities that spur participation.

Building an Arabic Sentiment Lexicon Using Semi-supervised Learning

Sentiment analysis is the process of determining a predefined sentiment from text written in a natural language with respect to the entity to which it is referring. A number of lexical resources are available to facilitate this task in English. One such resource is the SentiWordNet, which assigns sentiment scores to words found in the English WordNet. In this paper, we present an Arabic sentiment lexicon that assigns sentiment scores to the words found in the Arabic WordNet. Starting from a small seed list of positive and negative words, we used semi-supervised learning to propagate the scores in the Arabic WordNet by exploiting the synset relations. Our algorithm assigned a positive sentiment score to more than 800, a negative score to more than 600 and a neutral score to more than 6000 words in the Arabic WordNet. The lexicon was evaluated by incorporating it into a machine learning-based classifier. The experiments were conducted on several Arabic sentiment corpora, and we were able to achieve a 96% classification accuracy.

영어 트위터 감성 분석을 위한 SentiWordNet 활용 기법 비교

트위터 감성 분석은 트윗글의 감성을 긍정과 부정으로 분류하는 작업이다. 이 연구에서는 SentiWordNet(SWN) 감성 사전에 기반한 트윗글 감성 분석을 다룬다. SWN은 전체 영어 단어에 대해 단어의 의미별로 긍정, 부정의 감성 강도를 저장해 둔 감성 사전이다. 기존 SWN 기반 감성 분석 연구들은 문서에 출현하는 각 용어의 감성을 SWN으로부터 결정한 다음 이를 바탕으로 문서 전체의 감성을 결정하였는데, 그 방법들이 매우 다양하다. 예를 들어, 한 용어의 감성 결정 시 해당 용어의 SWN 내 의미별 긍정, 부정 감성 강도 차이들의 평균을 계산하거나 긍정과 부정 각각의 감성 강도 평균 혹은 최대값을 구하기도 하며, 문서 전체의 감성을 결정하는 경우에도 문서 내 용어들의 감성 값들에 대해 평균 혹은 최대값을 취하기도 하였다. 또한 SWN 내 형용사, 동사, 명사, 부사의 품사 집합 전체 혹은 특정 부분집합에 대해 위의 감성 결정 작업을 적용하기도 한다. 이처럼 기존 연구에서는 SWN 기반의 다양한 감성 자질 추출 절차가 시도되고 있으나 이들 자질 추출 기법 전반에 대한 성능 비교 연구는 찾기 힘들다. 이 연구에서는 SWN을 트위터 감성 분석에 활용하는 다양한 방법들을 일반화하는 절차들을 소개하고 각 방법들의 성능 비교 및 분석 결과를 제시한다. Twitter sentiment analysis is to classify a tweet (message) into positive and negative sentiment class. This study deals with SentiWordNet(SWN)-based twitter sentiment analysis. SWN is a sentiment dictionary in which each sense of an English word has a positive and negative sentimental strength. There has been a variety of SWN-based sentiment feature extraction methods which typically first determine the sentiment orientation (SO) of a term in a document and then decide SO of the document from such terms' SO values. For example, for SO of a term, some calculated the maximum or average of sentiment scores of its senses, and others computed the average of the difference of positive and negative sentiment scores. For SO of a document, many researchers employ the maximum or average of terms' SO values. In addition, the above procedure may be applied to the whole set (adjective, adverb, noun, and verb) of parts-of-speech or its subset. This work provides a comparative study on SWN-based sentiment feature extraction schemes with performance evaluation on a well-known twitter dataset.

Tweeting the Meeting: An In-Depth Analysis of Twitter Activity at Kidney Week 2011

In recent years, the American Society of Nephrology (ASN) has increased its efforts to use its annual conference to inform and educate the public about kidney disease. Social media, including Twitter, has been one method used by the Society to accomplish this goal. Twitter is a popular microblogging service that serves as a potent tool for disseminating information. It allows for short messages (140 characters) to be composed by any author and distributes those messages globally and quickly. The dissemination of information is necessary if Twitter is to be considered a tool that can increase public awareness of kidney disease. We hypothesized that content, citation, and sentiment analyses of tweets generated from Kidney Week 2011 would reveal a large number of educational tweets that were disseminated to the public. An ideal tweet for accomplishing this goal would include three key features: 1) informative content, 2) internal citations, and 3) positive sentiment score. Informative content was found in 29% of messages, greater than that found in a similarly sized medical conference (2011 ADA Conference, 16%). Informative tweets were more likely to be internally, rather than externally, cited (38% versus 22%, p<0.0001), thereby amplifying the original information to an even larger audience. Informative tweets had more negative sentiment scores than uninformative tweets (means −0.162 versus 0.199 respectively, p<0.0001), therefore amplifying a tweet whose content had a negative tone. Our investigation highlights significant areas of promise and improvement in using Twitter to disseminate medical information in nephrology from a scientific conference. This goal is pertinent to many nephrology-focused conferences that wish to increase public awareness of kidney disease.