Twitter Tweets Research Articles

Personality Detection on Twitter Social Media Using IndoBERT Method

Personality is the fundamental characteristic of human beings that makes humans unique. Because of these differences in human characteristics, personality becomes a benchmark for consideration in various recruitment processes. One way to predict personality is to apply an interview system or fill out questionnaires which often experience problems due to ineffectiveness in terms of time and cost. Results become inaccurate if prospective employees do not know themselves well. The big five personality method, divided into openness, conscientiousness, extraversion, agreeableness, and neuroticism, is widely used to predict personality. This study uses a deep learning method, IndoBERT, to detect personality based on five dimensions according to the big five personalities whose data is taken from Twitter tweets with crawling data. From the results of these studies, it is known that personality research using the IndoBERT method without a stemming process has a higher accuracy rate of 0.46.

A Two-Level Multi-Modal Analysis for Depression Detection From Online Social Media

According to World Health Organization statistics, depression is a prominent cause of concern worldwide, leading to suicide in the majority of these cases if left untreated. Nowadays, social media is a great place for users to express themselves through text, emoticons, images, etc., which reflect their thoughts and moods. This has opened up the possibility of studying social networks in order to better comprehend the mental states of their participants. The primary goal of the research is to examine Twitter user personas and tweets in order to uncover traits that may signal depressive symptoms among online users. A two-level depression detection method is proposed in which suspected depressed individuals are identified using social media features, personality traits, temporal and sentiment analysis of user biographies. Using the support vector machine classifier, these qualities are integrated with additional linguistic and topic features to achieve an accuracy of 89%. According to the research, effective feature selection and their combinations aid in enhancing performance.

Conditional Congressional communication: how elite speech varies across medium

AbstractElected representatives have more means of public-facing communication at their disposal than ever before. Several studies examine how representatives use individual mediums, but we lack a baseline understanding of legislators’ relative use patterns across platforms. Using a novel data set of the four most widely used forms of written, constituent-facing communication (press releases, e-newsletters, Facebook posts, and Twitter tweets) by members of the US House of Representatives in the 114th (2015–2017), 115th (2017–2019), and 116th (2019–2021) Congresses, we generate a baseline understanding of how representatives communicate across mediums. Our analyses show that institutional, legislator, and district characteristics correspond with differential use of mediums. These findings underscore why medium choice matters, clarifying how a researcher's choice of mediums might amplify the voices of certain legislators and dampen those of others. In addition, they provide guidance to other researchers on how to select the medium(s) that best correspond with different research aims.

Sentiment Analysis Based on Aspects Using FastText Feature Expansion and NBSVM Classification Method

Telkomsel is a service that the people of Indonesia widely use. Complaints from users referring to Telkomsel's service and signal aspects are often made in Twitter tweets with harsh or good language. This is done because users continue to demand to get better service. Therefore, an aspect-based sentiment analysis technique is needed to determine a person's view of each aspect, such as Telkomsel's service and signal aspects. Aspect-based sentiment analysis is a solution to find out the opinions of Telkomsel users based on their aspects. In its implementation, the NBSVM method is used as a classification model that is proven to work well compared to other methods, namely MNB and SVM. The implementation of the expansion of the FastText feature can affect the level of performance model, and the best results are obtained in the Top 1 feature on the signal aspect and Top 5 on the service aspect with a combination of Twitter corpus and news. In this study, the data used is unbalanced and has been handled by applying SMOTE and AdaBoost techniques to the FastText feature expansion model. Based on the results of the tests that have been carried out, SMOTE can handle data imbalances compared to AdaBoost. The performance results of the FastText feature expansion model after SMOTE are applied to get F1-Score 91.24% in the signal aspect and F1-Score 88.75% in the service aspect.

Hate Speech Hashtag Classification on Twitter Using the Hybrid Classifier Method

Hate speech on social media, especially Twitter, often takes the form of racism, sexism, or political interests aimed at certain individuals or groups. These actions can trigger crime, riots, violence and even resistance to individuals or groups. Therefore, we need a process of classifying a tweet whether it is hate speech or not to reduce the abuse that occurs on Twitter. The technology used in the classification of hate speech that is most commonly used is neural networks that require user data and meta data. In previous studies, the Naïve Bayes (NB) method has been used using the bigram, unigram and feature selection features with an accuracy of 80-85%. The k-Nearest Neighbor (kNN) method has also been used which has an accuracy of 70-85% on the clarification of hate speech by political figures. Meanwhile, the most widely used method is the Support Vector Machine (SVM) method with an accuracy of 70 to the highest 95%. To get a higher accuracy in the classification of hate speech, this study will perform a Hybrid Classifier on the Hate Speech Hashtag Classification process using a combination method of MLP, kNN, NB. The data used in this study are Twitter Tweets from November 2021 to June 2022 regarding trending hashtags. The average accuracy performance results obtained using MLP, kNN, NB were 72%, 63%,73% respectively. To improve the accuracy of the classification results of the three methods, a combination of methods using the Hybrid Classifier is carried out. Experimental results show Hybrid Classifier with voting method can increase accuracy up to 74%. It was found that the use of a hybrid can provide a better system performance than the 3 classifiers in its composition, namely kNN, NB and MLP

Discovering a tourism destination with social media data: BERT-based sentiment analysis

使用社交媒体数据发现旅游目的地：基于 bert 的情感分析。研究目的这项工作的主要目的是使用情感分析技术和来自 Twitter 和 Instagram 的数据来分析旅游目的地, 以便找到最具代表性的实体（或地点）和用户的感知（或方面）。研究设计/方法/途径我们使用 90,725 个 Instagram 帖子和 235,755 个 Twitter 推文来分析格拉纳达（西班牙）的旅游业, 以确定旅行者在两个社交媒体网站上提到的重要地点和看法。我们使用了几种方法对英语和西班牙语文本进行情感分类, 包括深度学习模型。研究发现测试集中的最佳结果是使用来自Transformers (BERT) 模型的双向编码器表示 (BERT) 用于西班牙语文本和Tweeteval 用于英语文本, 这些结果随后用于分析我们的数据集。然后可以确定最重要的实体和方面, 这反过来又为研究人员、从业人员、旅行者和旅游管理者提供了有趣的见解, 从而可以改进服务并制定更好的营销策略。研究局限性我们提出了一个用于执行情感分类的西班牙旅游 BERT 模型, 以及通过主题标签找到地点并揭示每个地点的重要负面方面的过程。实践意义该研究使管理人员和从业人员能够使用我们发布的西班牙旅游数据集实施西班牙-BERT 模型, 以便在应用程序中采用该数据集, 以找到正面和负面的看法。研究原创性本研究提出了一种如何在旅游领域应用情感分析的新方法。首先, 介绍了评估不同现有模型和工具的方法; 其次, 使用 BERT（深度学习模型）训练模型; 第三, 提出了如何通过标签识别目的地地点的接受度的方法, 最后通过实体和方面的识别来评估用户表达积极性（消极性）的原因。

Text Analysis of Evolving Emotions and Sentiments in COVID-19 Twitter Communication.

Scientists and regular citizens alike search for ways to manage the widespread effects of the COVID-19 pandemic. While scientists are busy in their labs, other citizens often turn to online sources to report their experiences and concerns and to seek and share knowledge of the virus. The text generated by those users in online social media platforms can provide valuable insights about evolving users’ opinions and attitudes. The objective of this research is to analyze text of such user disclosures to study human communication during a pandemic in four primary ways. First, we analyze Twitter tweet information, generated throughout the pandemic, to understand users’ communications concerning COVID-19 and how those communications have evolved during the pandemic. Second, we analyze linguistic sentiment concepts (analytic, authentic, clout, and tone concepts) in different Twitter settings (sentiment in tweets with pictures or no pictures and tweets versus retweets). Third, we investigate the relationship between Twitter tweets with additional forms of internet activity, namely, Google searches and Wikipedia page views. Finally, we create and use a dictionary of specific COVID-19-related concepts (e.g., symptom of lost taste) to assess how the use of those concepts in tweets are related to the spread of information and the resulting influence of Twitter users.The analysis showed a surprisingly lack of emotion in the initial phases of the pandemic as people were information seeking. As time progressed, there were more expressions of sentiment, including anger. Further, tweets with and without pictures and/or video had statistically significant differences in text sentiment characteristics. Similarly, there were differences between the sentiment in tweets and retweets and tweets. We also found that Google and Wikipedia searches were predictive of sentiment in the tweets. Finally, a variable representing a dictionary of COVID-related concepts was statistically significant when related to users’ Twitter influence score and number of retweets, illustrating the general impact of COVID-19 on Twitter and human communication. Overall, the results provide insights into human communication as well as models of human internet and social media use. These findings could be useful for the management of global challenges beyond, or different from, a pandemic.

Sentiment analysis using machine learning: Progress in the machine intelligence for data science

Sentiments and emotions of a person on social media is classified by the effective data science approaches. Data science is an inter-disciplinary domain that utilizes the scientific techniques, processes and algorithms to retrieve the sentiments from the twitter tweets. The classification of sentiments plays significant role in many application domain and with the assistance of the people emotions business industry can be developed accordingly. The sentiment extraction and the classification is attained by several approaches namely neuro-fuzzy and optimization algorithms. The technical contribution of this article is double feed forward neural network. These approaches face ineffective in classification when the real-time data contains numerous characters and stream of information. To attain proficient classification, double feed forward neural network is utilized and the output layer information is transmitted to the double layer of the network. Hence, the information’s are optimized and processed effectively, whereby the classification of sentiment is achieved. The entire process of the algorithm is carried and the acquired results are compared with the neuro-fuzzy and optimization algorithm. The DFFNN outperforms the existing algorithm in terms of classification parameters.

An Application to Detect Cyberbullying Using Machine Learning and Deep Learning Techniques.

Nowadays, a lot of people indulge themselves in the world of social media. With the current pandemic scenario, this engagement has only increased as people often rely on social media platforms to express their emotions, find comfort, find like-minded individuals, and form communities. With this extensive use of social media comes many downsides and one of the downsides is cyberbully. Cyberbullying is a form of online harassment that is both unsettling and troubling. It can take many forms, but the most common is a textual format. Cyberbullying is common on social media, and people often end up in a mental breakdown state instead of taking action against the bully. On the majority of social networks, automated detection of these situations necessitates the use of intelligent systems. We have proposed a cyberbullying detection system to address this issue. In this work, we proposed a deep learning framework that will evaluate real-time twitter tweets or social media posts as well as correctly identify any cyberbullying content in them. Recent studies has shown that deep neural network-based approaches are more effective than conventional techniques at detecting cyberbullying texts. Additionally, our application can recognise cyberbullying posts which were written in English, Hindi, and Hinglish (Multilingual data).

Opinion Mining from Social Media

Abstract— Twitter is a platform extensively utilized by humans to express their reviews and display opinions on one-of-akind activities. Opinion analysis is an approach to investigate statistics and retrieve opinion that it embodies. Twitter opinion analysis is ansoftware of opinion evaluation on information from Twitter (tweets), which will extract opinions conveyed by way of the consumer. In the past many years, the research on this field has always grown. The reason behind that is the difficult layout of the tweets which makes the processing difficult. The tweet format may be very small which generates a whole new measurement of problems like use of slang, abbreviations etc. In this paper, we intention to study some papers concerning research in opinion analysis on Twitter, describing the methodologies followed and fashions carried out, at the side of describing a generalized Python based method. Keywords— Opinion analysis, Machine Learning, Natural Language Processing, Python

#BingeDrinking—Using Social Media to Understand College Binge Drinking: Qualitative Study

BackgroundHazardous drinking among college students persists, despite ongoing university alcohol education and alcohol intervention programs. College students often post comments or pictures of drinking episodes on social media platforms.ObjectiveThis study aimed to understand one university’s student attitudes toward alcohol use by examining student posts about drinking on social media platforms and to identify opportunities to reduce alcohol-related harm and inform novel alcohol interventions.MethodsWe analyzed social media posts from 7 social media platforms using qualitative inductive coding based on grounded theory to identify the contexts of student drinking and the attitudes and behaviors of students and peers during drinking episodes. We reviewed publicly available social media posts that referenced alcohol, collaborating with undergraduate students to select their most used platforms and develop locally relevant search terms; all posts in our data set were generated by students associated with a specific university. From the codes, we derived themes about student culture regarding alcohol use.ResultsIn total, 1151 social media posts were included in this study. These included 809 Twitter tweets, 113 Instagram posts, 100 Greekrank posts, 64 Reddit posts, 34 College Confidential posts, 23 Facebook posts, and 8 YouTube posts. Posts included both implicit and explicit portrayals of alcohol use. Across all types of posts reviewed, positive drinking attitudes were most common, followed by negative and then neutral attitudes, but valence varied by platform. Posts that portrayed drinking positively received positive peer feedback and indicate that drinking is viewed by students as an essential and positive part of university student culture.ConclusionsSocial media provide a real-time picture of students’ behavior during their own and others’ heavy drinking. Posts portray heavy drinking as a normal part of student culture, reinforced by peers’ positive feedback on posts. Interventions for college drinking should help students manage alcohol intake in real time, provide safety information during alcohol use episodes, and raise student awareness of web-based privacy concerns and reputation management. Additional interventions for students, alumni, and parents are needed to address positive attitudes about and traditions of drinking.

The Predictive Power of a Twitter User’s Profile on Cryptocurrency Popularity

Microblogging has become an extremely popular communication tool among Internet users worldwide. Millions of users daily share a huge amount of information related to various aspects of their lives, which makes the respective sites a very important source of data for analysis. Bitcoin (BTC) is a decentralized cryptographic currency and is equivalent to most recurrently known currencies in the way that it is influenced by socially developed conclusions, regardless of whether those conclusions are considered valid. This work aims to assess the importance of Twitter users’ profiles in predicting a cryptocurrency’s popularity. More specifically, our analysis focused on the user influence, captured by different Twitter features (such as the number of followers, retweets, lists) and tweet sentiment scores as the main components of measuring popularity. Moreover, the Spearman, Pearson, and Kendall Correlation Coefficients are applied as post-hoc procedures to support hypotheses about the correlation between a user influence and the aforementioned features. Tweets sentiment scoring (as positive or negative) was performed with the aid of Valence Aware Dictionary and Sentiment Reasoner (VADER) for a number of tweets fetched within a concrete time period. Finally, the Granger causality test was employed to evaluate the statistical significance of various features time series in popularity prediction to identify the most influential variable for predicting future values of the cryptocurrency popularity.

Sentiment Analysis of Social Media Posts about Tourist Attractions: Black Sea Region Sample

Social media has become one of the essential advertising channels with the increase of users and potential customers. With the data obtained from social media, analyses on advertisements and customers are conducted in many areas. The tourism industry is one of the areas where social media has a significant impact. In this study, we used Twitter and sentiment analysis for ranking tourist attractions in terms of positive tweet rates. We collected tweets shared on Twitter regarding certain tourist attractions in the Black Sea Region of Turkey using social media analytics. We conducted sentiment analysis using RapidMiner data processing software and Operator Toolbox extension. They were used for collecting and processing the data and sentiment analysis of Twitter tweets. The touristic places selected within the scope of the research were determined as Abant, Amasra, Ayder Plateau, Hattusa, Kartalkaya, Persembe Plateau, Sumela Monastery, Uzungol and Yedigoller. Total of 1985 tweets were used for this study. After the sentiment analysis, we identified that the tourist attraction with the highest percentage of positive tweets was Abant (%76.70). After Abant, Ayder Plateau (%76.47) and Uzungöl (%75.68) were identified that the tourist attraction with the highest percentage of positive tweets, respectively.

Kata Bahasa Indonesia Penanda Register Twitter: Suatu Kajian Morfologi

This study was conducted to describe the formation and meaning of Indonesian words as Indonesian Twitter's tweet register markers. This research is a qualitative descriptive study with data collection methods using listen and note method. The referential equivalent method and the content analysis method with the presentation of the results of data analysis carried out using formal and informal methods are two methods used in data analysis. The data source is Twitter user comments in the six tweets of the @areajulid account with the most comments in 2021. The results show that there are five ways of forming Indonesian words as Twitter registers, namely the process of (1) affixation, including the words "mengjamet", "mengkesal", and "membagongkan"; (2) abbreviations include the words "mjb", "pen", "jamet", "lontang", "sat", "dahlah", and "monmaap"; (3) anagrams include the words "kane", "isilop", and "ngab"; (4) hybrids include the words "kenawhy", "akhlakn't", and "jujurly"; and (5) changes in the form of syllables in basic words including the words "anjir", "anjrit", "santuy", "gemoy", and "cangtip". The purposes of using the Indonesian word Twitter register marker are (1) to express feelings; (2) to make fun of; (3) to greet; and (4) to misspelt words.

Estimating Time-Series Changes in Social Sensitivity for COVID-19 @ Twitter in Japan

The global outbreak of COVID-19 is now putting enormous pressure on society to change our traditional social behavior. Government officials are also forced to make short-term decisions based on limited information for public health, and investor sentiment about the infection situation in each country has a significant impact on the stock market. In this paper, we attempt to visualize the time series of indexed social sentiment in Japan under the COVID-19 pandemic by using a neural network approach, and clarify changes in the sensitivity of citizens to the coronavirus. The sentiment was classified for Twitter tweets that matched the keywords for which the government was asked to restrict action, and sentiment trends were identified for the period from before the outbreak to the fifth wave of infection in Tokyo, Sapporo, Osaka, and Fukuoka. The indices obtained show a correlation with the number of infected cases by region and with national and local events, and in global cities such as Tokyo and Osaka as they experienced waves of infections and emergency declarations, sensitivity gradually became paralyzed, and parallel trends in sentiment waveforms were observed among regions.

Satiric parody through Indian English tweets in Twitter

AbstractThis paper examines satiric parody in Hinglish tweets that users created in response to the censorship in India of the James Bond movie Spectre. The analysis reveals the creativity with which users conflated two different cultures (Indian and Western) and languages (Hindi and English) in their tweets for a parodic portrayal of James Bond in an Indianized avatar and a satirical criticism of the underlying conservative ideology behind the censorship of the movie. Rather than taking the form of acrimonious debate on the issue of censorship, the #SanskariJamesBond tweets show how users dealt with the issue through satirical user‐generated Twitter memes. This study demonstrates that parody through code‐mixed tweets is an effective way of highlighting controversies, taking a stand on them, and, simultaneously, countering conflicting views through a collective public response on a social media platform.

A Review: Informing Disasters management by Using Twitter Tweets

Abstract: Recently social media plays a major role and providing information during disasters. This paper mainly focuses on how people used social media, especially Twitter, in response to the country's worst flood, Earthquake that had occurred recently. And these tweets collecting analyzed using machine learning algorithms such as Naïve Bayes, Random Forests, Decision Tree, sentiment Analysis during the disaster social media provides a surplus of information which includes information about the natural disaster, affected people's emotions, and relief efforts. And collect the tweets relating to disasters and build the sentimental classifier to categorize the user’s emotions during disaster based on various distress levels. Various analysis techniques are applied in collecting tweets. Keywords: Twitter Analysis, Sentiment Analysis, Random Forests, Naive Bayes, Decision Tree

An omicron variant tweeter sentiment analysis using NLP technique

Twitter is a miniature writing for a blog site which gives phase to individuals to share as well as communicate their perspectives about point, activities, items plus other medicinal harms. Tweets can be arranged keen on assorted classes reliant on their significance through the tip looked. NLP for wellbeing linked exploration be at present utilize in combination of tweet keen on positive as well as negative classes reliant on their approach utilizing normal language handling strategy. This paper contain execution of NLP (Bag of words) for message alliance reliant on twitter omicron tweet informational catalog utilizing sentiment preparing information utilizing twitter statistics set as well as suggest a plan to further expand categorization. Utilization of Lemmatization alongside NLP can further expand accuracy of characterization of tweets, via bountiful encouragement, pessimism as well as impartiality score of vocabulary present in tweet. For genuine effecting of this structure python through NLP plus twitter informational compilation be used. In this paper we are concerning feelings exploration in twitter tweet for omicron datasets to arrange the survey of all consumers whether it is positive, negative or impartial.

Using sentiment analysis on the tweets of the digital media platform Twitter to determine the extent of pilgrims’ satisfaction during the Covid-19 pandemic

This study aimed to discover the extent of pilgrims’ satisfaction with the services provided to them during the Hajj of the year 2020 AD through the participation of pilgrims and witnesses of the Hajj season on the platform of Twitter. Sentiment analysis was conducted for the period of July 15, 2020 AD to August 21, 2020 AD. Data was extracted using sentiment analysis for the tags of #Hajj, #Jamarat, #Kaaba, #Tawaf, #Mecca, #Arafa, #Muzdalifah, #pilgrims, #Mina. 137784 tweets were written using these tags, of which 36627 tweets were Arabic and analyzed in this study. Sentiment analysis was performed in two stages. The first stage was the analysis of tweets based on the polarity of feelings, and the second stage was a detailed analysis of the type of positive or negative feelings of the tweets. The study resulted in a number of findings, the most important of which is that the positive feelings about the 2020 pilgrimage outperformed the negative feelings by a considerable margin, which is a strong indicator of the pilgrims’ satisfaction with the services provided during the Hajj season. Additionally, as the 2020 Hajj took place during the Covid-19 pandemic, the satisfaction of pilgrims and Muslims in general was not only emotionally motivated; there was also a cognitive awareness of the exceptional circumstances of Hajj due to the pandemic and a great understanding of the wise position of the Kingdom of Saudi Arabia in restricting Hajj to pilgrims inside. This was in compliance with the precautionary health measures that guarantee the safety of pilgrims during their performance of Hajj rituals. The current study clearly indicates that negative feelings may not result from a negative impression and do not result in negative behavior, but that these feelings may be instantaneous and result from certain exceptional circumstances in which the cognitive perception of the client (the pilgrim) agrees on the negative feeling. Hence, it is necessiry to identify the detailed feelings of the texts. In addition, the current study found that it is necessary to integrate content analysis with sentiment analysis to reach a comprehensive understanding of the significance of feelings. It may result in a completely different feeling that is not categorized within the Plutchik’s Wheel.

The Analysis of Impoliteness within Grammar Nazi Context in Twitter Tweets

This research focuses on one of the phenomena namely the Grammar Nazi phenomenon. In the present paper, the Grammar Nazi phenomenon was a phenomenon where people did not hesitate to criticize other people's grammatical errors. This phenomenon of Grammar Nazi was associated with the theory of Impoliteness Strategy by Culpeper (1996). This research is a qualitative one, where 100 data employed were processed first using a coding technique. The results obtained from this study are 55% of the data using the Bald on Record strategy, 18% of the data using the Sarcasm or Mock Politeness strategy, 16% of the data using the Negative Impoliteness strategy, 11% of the data using the Positive Impoliteness strategy, while 0% data using the Withhold Politeness strategy. It was also found that 61% of the data of the Grammar Nazi phenomenon used verbal bullying only to attack the face of authors of the posts, while the remaining 39% of the data used verbal bullying with intentions to correct grammatical errors, hence providing benefits for authors of the posts. The Grammar Nazi phenomenon also has a different impact on everyone. It can be seen from the characteristics of the utterances used by the Grammar Nazi.Keywords: Grammar Nazi, Grammar Nazi Phenomenon, Impoliteness Strategy, Five Strategies, Twitter.