Real Time Sentimental Analysis on Twitter

Madhu, K. S.; Reddy, B Chakradhar; Damarukanadhan, CH; Polireddy, M; Ravinder, N.

doi:10.1109/icict50816.2021.9358772

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…There are many HDFS use-cases for real-time scenarios (though none that we could find matching to our specific purpose). K. Madhu et al [20], S. Mishra et al [21], K. Aziz et al [22] and R. Kamal et al [23] all perform real-time data analysis on tweets from Twitter that are stored in HDFS. S. Kumar et al [14] use HDFS to store real-time massive amounts of data produced by autonomous vehicles sensors.…”

Section: Persistent Storagementioning

confidence: 99%

Parameterization and Performance Analysis of a Scalable, near Real-Time Packet Capturing Platform

Oliveira,

Pedrosa,

Rufino

et al. 2024

Systems

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The rapid evolution of technology has fostered an exponential rise in the number of individuals and devices interconnected via the Internet. This interconnectedness has prompted companies to expand their computing and communication infrastructures significantly to accommodate the escalating demands. However, this proliferation of connectivity has also opened new avenues for cyber threats, emphasizing the critical need for Intrusion Detection Systems (IDSs) to adapt and operate efficiently in this evolving landscape. In response, companies are increasingly seeking IDSs characterized by horizontal, modular, and elastic attributes, capable of dynamically scaling with the fluctuating volume of network data flows deemed essential for effective monitoring and threat detection. Yet, the task extends beyond mere data capture and storage; robust IDSs must integrate sophisticated components for data analysis and anomaly detection, ideally functioning in real-time or near real-time. While Machine Learning (ML) techniques present promising avenues for detecting and mitigating malicious activities, their efficacy hinges on the availability of high-quality training datasets, which in turn poses a significant challenge. This paper proposes a comprehensive solution in the form of an architecture and reference implementation for (near) real-time capture, storage, and analysis of network data within a 1 Gbps network environment. Performance benchmarks provided offer valuable insights for prototype optimization, demonstrating the capability of the proposed IDS architecture to meet objectives even under realistic operational scenarios.

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Section: Persistent Storagementioning

confidence: 99%

Parameterization and Performance Analysis of a Scalable, near Real-Time Packet Capturing Platform

Oliveira,

Pedrosa,

Rufino

et al. 2024

Systems

View full text Add to dashboard Cite

show abstract

“…We run the programme using the Python package "yfinance" and extract the bitcoin data in USD as our bitcoin data for time series analysis from Figure 2. We first requested a "Twitter developer" [19] account based on Figure 3. Using a Python module named "tweepy," we later produced consumer key, consumerSecret, accessToken, and accessSecret to authenticate on Twitter.…”

Section: Data Collectionmentioning

confidence: 99%

Bitcoin trading indicator: a machine learning driven real time bitcoin trading indicator for the crypto market

et al. 2023

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As opposed to other fiat currencies, bitcoin has no relationship with banks. Its price fluctuation is largely influenced by fresh blocks, news, mining information, support or resistance levels, and public opinion. Therefore, a machine-learning model will be fantastic if it learns from data and tells or indicates if we need to purchase or sell for a little period. In this study, we attempted to create a tool or indicator that can gather tweets in real-time using tweepy and the Twitter application programming interface (API) and report the sentiment at the time. Using the renowned Python module "FBProphet," we developed a model in the second phase that can gather historical price data for the bitcoin to US dollar (BTCUSD) pair and project the price of bitcoin. In order to provide guidance for an intelligent forex trader, we finally merged all of the models into one form. We traded with various models for a very little number of days to validate our bitcoin trading indicator (BTI), and we discovered that the combined version of this tool is more profitable. With the combined version of the instrument, we quickly and with little error root mean square error (RMSE: 1,480.58) generated a profit of $1,000.71 USD.

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Feasible Sentiment Analysis of Real Time Twitter Data

Karuna,

Anvesh,

Sharath Singh

et al. 2023

E3S Web Conf.

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Sentiment analysis plays a significant role in understanding public opinion, trends, and sentiments expressed on social media platforms. In this paper, we focus on performing sentiment analysis on real-time Twitter data to gain insights into the sentiments related to specific topics or events, we collect a stream of tweets based on predefined keywords or hashtags. The collected tweets undergo pre-processing steps to clean and standardize the text for sentiment analysis. We employ machine learning classify the sentiments expressed in tweets, utilizing sentiment lexicons and training data as references. Real-time sentiment analysis is performed as new tweets are collected, enabling continuous monitoring and analysis of public sentiment. The sentiment analysis results are visualized through informative visualizations such as sentiment distribution charts and sentiment trends over time. Additionally, we focus on topic-specific analysis by filtering tweets based on relevant keywords or hashtags, providing deeper insights into sentiments related to specific subjects. The paper faces challenges such as noisy and informal text, ambiguity in sentiment expression, and handling large volumes of real-time data. Addressing these challenges, we aim to develop an effective sentiment analysis system that provides valuable insights into public sentiment and supports decision-making processes in various domains.

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Real Time Sentimental Analysis on Twitter

Cited by 8 publications

References 7 publications

Parameterization and Performance Analysis of a Scalable, near Real-Time Packet Capturing Platform

Parameterization and Performance Analysis of a Scalable, near Real-Time Packet Capturing Platform

Bitcoin trading indicator: a machine learning driven real time bitcoin trading indicator for the crypto market

Feasible Sentiment Analysis of Real Time Twitter Data

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