An approach to analyse and Forecast Social media data using Machine Learning and Data Analysis

Dansana, Debabrata; Adhikari, Janmejoy Das; Mohapatra, Manas; Sahoo, Subhashree

doi:10.1109/iccsea49143.2020.9132895

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…Kowsari et al [ 28 ] proposed a deep learning model to analyze the mental status of patients based on Reddit posts. Many of the previous systems have applied insufficient data sets for depression and anxiety, which may produce imprecise results and thus perhaps mislead healthcare workers [ 29 ]. Artificial intelligence (AI) based techniques have been proposed to examine patients' posts on social media platforms and recognize serious problems in those patients [ 27 ].…”

Section: Related Studiesmentioning

confidence: 99%

A Novel Text Mining Approach for Mental Health Prediction Using Bi-LSTM and BERT Model

Zeberga

Attique

Shah

et al. 2022

Computational Intelligence and Neuroscience

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With the current advancement in the Internet, there has been a growing demand for building intelligent and smart systems that can efficiently address the detection of health-related problems on social media, such as the detection of depression and anxiety. These types of systems, which are mainly dependent on machine learning techniques, must be able to deal with obtaining the semantic and syntactic meaning of texts posted by users on social media. The data generated by users on social media contains unstructured and unpredictable content. Several systems based on machine learning and social media platforms have recently been introduced to identify health-related problems. However, the text representation and deep learning techniques employed provide only limited information and knowledge about the different texts posted by users. This is owing to a lack of long-term dependencies between each word in the entire text and a lack of proper exploitation of recent deep learning schemes. In this paper, we propose a novel framework to efficiently and effectively identify depression and anxiety-related posts while maintaining the contextual and semantic meaning of the words used in the whole corpus when applying bidirectional encoder representations from transformers (BERT). In addition, we propose a knowledge distillation technique, which is a recent technique for transferring knowledge from a large pretrained model (BERT) to a smaller model to boost performance and accuracy. We also devised our own data collection framework from Reddit and Twitter, which are the most common social media sites. Finally, we employed word2vec and BERT with Bi-LSTM to effectively analyze and detect depression and anxiety signs from social media posts. Our system surpasses other state-of-the-art methods and achieves an accuracy of 98% using the knowledge distillation technique.

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Section: Related Studiesmentioning

confidence: 99%

A Novel Text Mining Approach for Mental Health Prediction Using Bi-LSTM and BERT Model

Zeberga

Attique

Shah

et al. 2022

Computational Intelligence and Neuroscience

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“…The authors of [5] explored sentiment analysis and machine learning techniques applied to Twitter data related to Goods and Service Tax (GST) collected by web scraping. The study calculated the sentiments of positive, negative, and neutral tweets and used data visualization to find patterns.…”

Section: Related Workmentioning

confidence: 99%

A New Approach for Carrying Out Sentiment Analysis of Social Media Comments Using Natural Language Processing

Ranjan,

Tiwari,

Sattar

et al. 2024

RAiSE-2023

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“…Another way of applying Python is through the Text Blob Tool which analyzes the sentiments of tweets [10]. Other tools in Python enable web-scraping activities such as Scrapy and Pandas [11], SpaCy [12], and Python's Natural Language Toolkit [13]. However, to extract information from a website by only having knowledge of Python is not sufficient.…”

Section: Wds: a Big Data Sourcementioning

confidence: 99%

The Value of Web Data Scraping: An Application to TripAdvisor

Barbera

Araújo

Fernandes

2023

BDCC

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Social Media Analytics (SMA) is more and more relevant in today’s market dynamics. However, it is necessary to use it wisely, either in promoting any kind of product/brand, or interacting with customers. This requires its effective understanding and monitoring. One way is through web data scraping (WDS) tools that allow to select sites and platforms to compare them in their performances. They can optimize extraction of big data published on social media. Due to current challenges, a sector that can particularly take advantage of this source is tourism (and its related sectors). This year has the hope of tourism’s revival after a pandemic whose impacts are still affecting several activities. Many traders and entrepreneurs have already used these versatile tools. However, do they really know their potential? The present study highlights the use of WDS to collect data from TripAdvisor’s social pages. Besides comparing competitors’ performance, companies also gain new knowledge of unnoticed preferences/habits. This contributes to more interesting innovations and results for them and for their customers. The approach used here is based on a project for smart tourism consultancy, from the identification of a gap in our region, to aid tourism organizations to enhance their digital presence and business model. Many things can be detected in this big source of unstructured data very quickly and easily without programming. Moreover, exploring code, either to refine the web scraper or connect it with other platforms/apps, can be an object of future research to leverage consumer behavior prediction for more advanced interactions.

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An approach to analyse and Forecast Social media data using Machine Learning and Data Analysis

Cited by 8 publications

References 5 publications

A Novel Text Mining Approach for Mental Health Prediction Using Bi-LSTM and BERT Model

A Novel Text Mining Approach for Mental Health Prediction Using Bi-LSTM and BERT Model

A New Approach for Carrying Out Sentiment Analysis of Social Media Comments Using Natural Language Processing

The Value of Web Data Scraping: An Application to TripAdvisor

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