Depressive Bangla Text Detection from Social Media Post Using Different Data Mining Techniques

Solace in Social Media: Women Unite Under COVID-19

Now-a-days, Social media platforms like Facebook, Twitter etc. are becoming more and more popular to express feelings and thoughts. People not only share their happy moments on these platforms but also share their feelings when they are extremely depressed. Analyzing these social media posts, one’s mental condition can be detected whether he/she is happy, sad or angry at a particular time using sentiment analysis in natural language processing. Most of the research in this field is based on English language and the accuracy of sentiment analysis from Bengali language is not very high. So, our purpose is to work on this field using Bengali dataset collected from different social media posts and make the sentiment detection more accurate so that this work can be used to build a system that can be used in the mental health sector of our country. In this research, we have first collected social media data. After applying different data preprocessing techniques, we have made a number of feature selection and extraction combinations. We have applied some basic and advanced machine learning and deep learning algorithms on each of these to find the best combination and algorithm by monitoring the highest achieved accuracy.

The research is dealt with critical discourse analysis on the politicians' social media posts which is mainly aimed to Investigate the hoax phenomenon especially the content of politicians on the social media posts. The data of the research is taken from the politician status social media through downloading the the pictures, reading the content of pictures carefully, analyzing the hoax posts by using Lynda Walsh (2006), select and identify the hoax posts phenomenon of the politicians' social media posts and to make the data be stronger, the researcher also did an interview with some people and it is acknowledged that the hoax phenomenon of the politicians' social media posts has brought the anxiety in the society life in approaching the presidential election 2019. It has many the power of context that made the society believed on it and to critical the case it is used the theory of Van Djik's framework 1988 and 2001 that focus critical discourse as language, ideology and power.The conclusion of this research is there were many various impacts and the influences toward the society who do not understand in choosing which is a hoax news or a fact news on the social media because in all the pictures of the hoax posts that the researcher put on this thesis, it has proved based on interviewing some people and it has declared by communication and technology that the hoax posts phenomenon that has shared by the society and the politician is really problematic towards the irrational people.

The purpose of this article is to present a framework for capturing and analyzing social media posts using a sentiment analysis tool to determine the views of the general public towards autonomous mobility. The paper presents the systems used and the results of this analysis, which was performed on social media posts from Twitter and Reddit. To achieve this, a specialized lexicon of terms was used to query social media content from the dedicated application programming interfaces (APIs) that the aforementioned social media platforms provide. The captured posts were then analyzed using a sentiment analysis framework, developed using state-of-the-art deep machine learning (ML) models. This framework provides labeling for the captured posts based on their content (i.e., classifies them as positive or negative opinions). The results of this classification were used to identify fears and autonomous mobility aspects that affect negative opinions. This method can provide a more realistic view of the general public’s perception of automated mobility, as it has the ability to analyze thousands of opinions and encapsulate the users’ opinion in a semi-automated way.

Huge amount of user generated contents are being created in major social media everyday. It’s very difficult for users to quickly grasp the most important topics from such big data. Document clustering techniques are often used for topic detection from news, but it’s still challenging for social media. Firstly, since social media posts are usually very short, it’s hard to capture their semantic meanings. Secondly, given huge amount of social media posts, clustering effectiveness becomes unacceptable. Among agglomerative hierarchical clustering methods, centroid clustering is much more efficient, but with the issue of inversion or reversals. In this paper, we propose to detect popular topics from social media posts using dot-product similarity based centroid clustering of their word embeddings. Firstly, we extract keywords in posts with word segmentation, where documents are represented by word embedding of keywords. Secondly, various topics are extracted from the clustering results of social media posts by calculating dot-product similarity of their word embeddings. Finally, the popularity of each topic is estimated by the aggregate sentiment ratings from user replies. From the experimental results on PTT discussion forum, centroid clustering with dot-product similarity of word embeddings achieves better clustering efficiency with comparable effectiveness in terms of Adjusted Rand Index (ARI) and Adjusted Mutual Information (AMI). Further investigation is needed to verify the effectiveness in different social media sources.

Sentiment analysis plays a pivotal role in understanding the sentiments and opinions expressed in textual data, offering valuable insights into various domains, including tourism. In this study, we present a comprehensive review of sentiment analysis techniques applied to tourism reviews using machine learning algorithms. The abundance of user-generated content on tourism platforms has made sentiment analysis an indispensable tool for businesses and researchers alike. By leveraging machine learning algorithms, researchers can extract sentiments from vast amounts of textual data efficiently and accurately. This review outlines the key methodologies and approaches utilized in sentiment analysis of tourism reviews. It discusses preprocessing techniques such as text tokenization, stop-word removal, and stemming, which are crucial for preparing textual data for analysis. Furthermore, it examines various machine learning algorithms employed for sentiment classification, including Naive Bayes, Support Vector Machines, and Recurrent Neural Networks. Additionally, the review delves into feature extraction methods such as bag-of-words, TF-IDF, and word embeddings, highlighting their impact on sentiment analysis accuracy. Moreover, it explores the challenges and limitations associated with sentiment analysis in the tourism domain, such as sarcasm detection and language nuances.

PurposeThe widespread acceptance of various social platforms has increased the number of users posting about various services based on their experiences about the services. Finding out the intended ratings of social media (SM) posts is important for both organizations and prospective users since these posts can help in capturing the user’s perspectives. However, unlike merchant websites, the SM posts related to the service-experience cannot be rated unless explicitly mentioned in the comments. Additionally, predicting ratings can also help to build a database using recent comments for testing recommender algorithms in various scenarios.Design/methodology/approachIn this study, the authors have predicted the ratings of SM posts using linear (Naïve Bayes, max-entropy) and non-linear (k-nearest neighbor, k-NN) classifiers utilizing combinations of different features, sentiment scores and emotion scores.FindingsOverall, the results of this study reveal that the non-linear classifier (k-NN classifier) performed better than the linear classifiers (Naïve Bayes, Max-entropy classifier). Results also show an improvement of performance where the classifier was combined with sentiment and emotion scores. Introduction of the feature “factors of importance” or “the latent factors” also show an improvement of the classifier performance.Originality/valueThis study provides a new avenue of predicting ratings of SM feeds by the use of machine learning algorithms along with a combination of different features like emotional aspects and latent factors.

In defense-in-depth, humans have always been the weakest link in cybersecurity. However, unlike common threats, social engineering poses vulnerabilities not directly quantifiable in penetration testing. Most skilled social engineers trick users into giving up information voluntarily through attacks like phishing and adware. Social Engineering (SE) in social media is structurally similar to regular posts but contains malicious intrinsic meaning within the sentence semantic. In this paper, a novel SE model is trained using a Recurrent Neural Network Long Short Term Memory (RNN-LSTM) to identify well-disguised SE threats in social media posts. We use a custom dataset crawled from hundreds of corporate and personal Facebook posts. First, the social engineering attack detection pipeline (SEAD) is designed to filter out social posts with malicious intents using domain heuristics. Next, each social media post is tokenized into sentences and then analyzed with a sentiment analyzer before being labelled as an anomaly or normal training data. Then, we train an RNN-LSTM model to detect five types of social engineering attacks that potentially contain signs of information gathering. The experimental result showed that the Social Engineering Attack (SEA) model achieves 0.84 in classification precision and 0.81 in recall compared to the ground truth labeled by network experts. The experimental results showed that the semantics and linguistics similarities are an effective indicator for early detection of SEA.

World wide web is abundant with various types of information such blogs, social media posts, news articles. With this type of magnitude of online content, there is a need to deeply understand the insights of it in order to make use of the information for practical applications such as event detection, polarity, sentiment analysis and so on. Natural Language Processing (NLP) is the study of such information which is used for text classification, sentiment analysis, clustering of similar text. NLP makes use of linguistic knowledge and build machine learning models to analyse textual information. NLP finds its way in various applications like classification of online review into positive and negative without actually reading the reviews and feedback. For text analysis, there should be a way to quantify the text based on its frequency of occurrence, correlation with neighbouring words, contextual similarity of words, etc. One such way is word embedding. This study applies various word embedding techniques on tweets of popular news channels and clusters the resultant vectors using K-means algorithm. From this study, it is found out that Bidirectional Encoder Representations from Transformers (BERT) has achieved highest accuracy rate when used with K-means clustering.

A Benchmark Dataset for Cricket Sentiment Analysis in Bangla Social Media Text

Driven by the need to enhance user traffic on social media (SM) platforms for increasing their advertising revenues, SM platforms are experimenting with new content creation features. However, it is unclear if such initiatives are also beneficial for SM profile owners such as influencers, who are the prime content creators on the SM platforms who use SM posts to build their influence within their network of followers. Our study investigates the effect of introducing one such new SM feature: the “story” on the creation and consumption of SM posts. Leveraging social penetration theory, we hypothesize the influence of introducing story feature on (1) the frequency of SM post creation by profile owners and (2) the extent of follower engagement with SM posts. Employing a quasi-experimental design, we find that the introduction of the story feature reduces the frequency of SM post creation, but the enhanced self-disclosure through the story feature increases follower engagement with the SM posts. However, these effects are moderated by the situating culture of the SM communities: while low-power-distance cultures value profile owners’ self-disclosure, high-power-distance cultures exhibit a mixed influence. Advancing literature on social penetration theory and SM user engagement, our study demonstrates that new self-disclosive SM content creation features do not necessarily benefit all the concerned stakeholders and that the effectiveness of such features might vary from one community to another. Hence, the intended impact of introducing new SM features needs to be carefully evaluated by SM platforms in a holistic manner.

Development in the field of opinion mining and sentiment analysis has been rapid and aims to explore views or texts on various social media sites through machine-learning techniques with the sentiment, subjectivity analysis and calculations of polarity. Sentiment analysis is a natural language processing strategy used to decide if the information is positive, negative, or neutral and it is frequently performed on literature information to help organizations screen brand, item sentiment in client input, and comprehend client needs. In this paper, two strategies for sentiment analysis is proposed for word embedding and a bag of words on Urdu and English tweets. Word embedding is a notable arrangement of procedures that can remember words linguistics dependent on the spread theory which expresses that word is utilized and happens within the same settings tend to indicate comparable implications. Bag of words is an approach used in natural language processing to retrieve information and features from written documents. For the bag of words, machine learning techniques like naive bayes, decision tree, k-nearest neighbor, and support vector machine is used to enhance the accuracy. For word embedding the neural network technique is proposed by the combination of recurrent neural network (RNN) with long-short term memory (LSTM) for sentimental analysis of tweets. Datasets of Urdu and English tweets are used for negative and positive classification tweets with machine learning techniques. The contribution of this paper involves the implementation of a hybrid approach that focused on a sentiment analyzer to overcome social network challenges and also provided the comparative analysis of different machine learning algorithms. The results indicate improvement while using the combination of RNN with the help of LSTM showed accuracy 87% on the Urdu dataset and 92% on the English dataset.

PurposeOnline influencers are increasingly used by brands around the globe to establish brand communication. This study aims to investigate the characteristics of social media content in terms of presentation style and brand communication among online influencers in China. The authors identified how characteristics of social media posts influence young consumers’ engagement with the posts.Design/methodology/approachThe authors analyzed 1,779 posts from the Sina Weibo accounts of ten top-ranked online influencers by combining traditional content analysis with Web data crawling of audience engagement with social media posts.FindingsOnline influencers in China more frequently used photos than videos to communicate with their social media audience. Altogether 8% and 6% of posts carried information about promotion and event, respectively. Posts with promotional incentives as well as event information were more likely to engage audiences. Altogether 22% of the sampled social media posts mentioned brands. Posts with brand information, however, were less likely to engage audiences. Furthermore, having long text is more effective than photos/images in generating likes from social media audiences.Originality/valueCombining content analysis of social media posts and engagement analytics obtained via Web data crawling, this study is, to the best of the authors’ knowledge, one of the first empirical studies to analyze influencer marketing and young consumers’ reactions to social media in China.

Natural language processing (NLP) has been changed by neural networks, which make it possible to analyze social media data in more detail, even though it is very large, not organized, and changes all the time. This abstract looks at how neural networks can be used in natural language processing (NLP) to analyze social media. Every day, social media sites produce huge amounts of written content that covers a wide range of topics, feelings, and writing styles. The subtleties and complexity of this data are often too much for traditional NLP methods to handle. Neural networks, on the other hand, offer strong answers because they can learn complex patterns and models from raw text. Sentiment analysis is one of the main ways that neural networks are used in social media research. Deep learning designs like recurrent neural networks (RNNs) or more advanced models like transformer-based architectures (e.g., BERT, GPT) can correctly describe how people feel about social media posts. Businesses can use this feature to find out what the public thinks, keep track of how people feel about their brand, and spot new trends in real time. Neural networks are useful for more than just analyzing mood. They can also help with named entity recognition (NER), topic modeling, and even figuring out humor and irony in text, which is hard for traditional rule-based systems because they rely on set rules and definitions. Also, neural networks are great at processing data in more than one language, which is important because social media is used all over the world. When learned on big datasets, models can be used across languages, giving us information about different language groups without a lot of human tweaking. Another big benefit of neural network methods is that they can be scaled up or down, which is very important for handling the huge amounts of data that social media sites create.

<p><strong><em>The aim</em></strong><em> of the work is to outline the most effective communication channels with residents in smart cities.</em><em></em></p><p><strong><em>Research methodology.</em></strong><em> Both theoretical and empirical research methods were used in the research process. The research methodology consisted of several stages. The first was a comprehensive literature review to understand the current state of knowledge in the fields of smart city development, social media, and urban communication. This involved analyzing existing studies, articles, and reports on how social media is used within smart cities for engaging with residents. The next was data collection. This method was used to build a dataset of types of communication with residents in smart cities. After collecting a dataset of posts in social media, we extract relevant features that can be used to provide effective communication between local authorities and audience. These features include the frequency of writing posts in social media, the types of answers and comments, the explaining of sensitive topics. </em></p><p><strong><em>Results.</em></strong><em> It was found out that effective communication with residents in smart cities should include such features: the use of digital platforms for communication (such as mobile apps, social media, or online forums) have seen increased engagement from residents. This could manifest in higher participation rates in city surveys, community events, or feedback mechanisms. Also it was examined that effective communication channels can lead to higher resident satisfaction. This might be due to quicker responses to complaints, more efficient service delivery, or a greater sense of being heard and considered by city administrations.</em></p><p><strong><em>Novelty.</em></strong><em> The novelty of this work is the proposed effective types of communication with residents in smart cities. For example,</em> <em>younger residents may prefer social media or apps, while older residents might rely on more traditional methods like community newsletters or public meetings. The next studies might also explore how communication technologies impact the social and cultural fabric of urban communities, possibly affecting community bonding, local culture preservation, and social inclusivity.</em></p><p><strong><em>Practical meaning.</em></strong><em> Effective communication channels can lead to higher resident satisfaction. This might be due to quicker responses to complaints, more efficient service delivery, or a greater sense of being heard and considered by city administrations. Moreover, smart city communication strategies might be particularly effective in enhancing public safety. Quick dissemination of information regarding emergencies, health alerts, or public safety incidents can be a significant benefit.</em></p><p><strong><em>Key words:</em></strong><em> social media, communication, audience, smart cities.</em></p>