Analysis of Twitter Specific Preprocessing Technique for Tweets

Ramachandran, Dharini; Parvathi, R.

doi:10.1016/j.procs.2020.01.083

Cited by 37 publications

(12 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Text preprocessing is often recommended for data optimization when working with a large number of unstructured entries. The impetus is high on social media data that contains informal language, repetitions, URLs, and abbreviations [26], which creates noise, and as such clouds the 'real' sentiment behind the opinion. Figure 3 illustrates common steps for Text Preprocessing but can be expanded depending on the data type.…”

Section: Text Preprocessingmentioning

confidence: 99%

Sentiment Analysis of Cyber Security Content on Twitter and Reddit

Thapa¹

2022

Data Mining and Machine Learning

View full text Add to dashboard Cite

Sentiment Analysis provides an opportunity to understand the subject(s), especially in the digital age, due to an abundance of public data and effective algorithms. Cybersecurity is a subject where opinions are plentiful and differing in the public domain. This descriptive research analyzed cybersecurity content on Twitter and Reddit to measure its sentiment, positive or negative, or neutral. The data from Twitter and Reddit was amassed via technology-specific APIs during a selected timeframe to create datasets, which were then analyzed individually for their sentiment by VADER, an NLP (Natural Language Processing) algorithm. A random sample of cybersecurity content (ten tweets and posts) was also classified for sentiments by twenty human annotators to evaluate the performance of VADER. Cybersecurity content on Twitter was at least 48% positive, and Reddit was at least 26.5% positive. The positive or neutral content far outweighed negative sentiments across both platforms. When compared to human classification, which was considered the standard or source of truth, VADER produced 60% accuracy for Twitter and 70% for Reddit in assessing the sentiment; in other words, some agreement between algorithm and human classifiers. Overall, the goal was to explore an uninhibited research topic about cybersecurity sentiment.

show abstract

Section: Text Preprocessingmentioning

confidence: 99%

Sentiment Analysis of Cyber Security Content on Twitter and Reddit

Thapa¹

2022

Data Mining and Machine Learning

View full text Add to dashboard Cite

show abstract

“…Once the tweets were collected, NLTK 2 with pip package manager in Python has been used for processing the text in tweets. This process includes the removal of extra places, stop words, URL, emojis, tokenization and lemmatization [ 15 ].…”

Section: Dataset Descriptionmentioning

confidence: 99%

Predicting Geolocation of Tweets: Using Combination of CNN and BiLSTM

Mahajan

Mansotra

2021

Data Sci. Eng.

View full text Add to dashboard Cite

Twitter is one of the most popular micro-blogging and social networking platforms where users post their opinions, preferences, activities, thoughts, views, etc., in form of tweets within the limit of 280 characters. In order to study and analyse the social behavior and activities of a user across a region, it becomes necessary to identify the location of the tweet. This paper aims to predict geolocation of real-time tweets at the city level collected for a period of 30 days by using a combination of convolutional neural network and a bidirectional long short-term memory by extracting features within the tweets and features associated with the tweets. We have also compared our results with previous baseline models and the findings of our experiment show a significant improvement over baselines methods achieving an accuracy of 92.6 with a median error of 22.4 km at city level prediction.

show abstract

“…Next, the last one is Stopword Removal. Stopword itself is a word that is in the data but is less helpful in the process of tweeting analysis [23].…”

Section: Twitter Apimentioning

confidence: 99%

Analysis of twitter sentiment in COVID-19 era using fuzzy logic method

2021

joscex

View full text Add to dashboard Cite

The sentiment is an assessment of attitudes towards certain events or things. Collecting opinion is known as a sentiment from existing data. This technique can also help analyze the opinions given by people in assessing certain objects. The best available source for gathering sentiment is the internet. In the era of the Covid-19 pandemic, many people access social media, especially Twitter to give their opinion on certain objects. Twitter is known as the social media that is accessed by users to post their opinions online. By using soft computing, especially fuzzy logic, it is possible to design, create and build bots that can analyze user opinions on Twitter. This model is used for data sentiment analysis on Twitter. The results showed that the sentiment analysis during the Covid-19 period was still dominant with positive tweets. As many as 48% of tweets are positive, 30% are negative tweets and 22% are neutral tweets. The use of applications to identify tweet sentiment during Covid-19 uses a combination of fuzzy logic methods with artificial intelligence. With the help of the Twitter API, you can get tweet data during the Covid-19 pandemic so you can find out the frequently used tweet sentiments.

show abstract

Analysis of Twitter Specific Preprocessing Technique for Tweets

Cited by 37 publications

References 3 publications

Sentiment Analysis of Cyber Security Content on Twitter and Reddit

Sentiment Analysis of Cyber Security Content on Twitter and Reddit

Predicting Geolocation of Tweets: Using Combination of CNN and BiLSTM

Analysis of twitter sentiment in COVID-19 era using fuzzy logic method

Contact Info

Product

Resources

About