Authors :
Archis Khuspe; Tejas Gaikwad; Agnibha Sarkar; Medha Wyawahare; Ankita Kumari; Abhay Chopde
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/4arwn6nu
Scribd :
https://tinyurl.com/4b4es3ub
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR227
Abstract :
Sentiment analysis is a crucial field that deals
with the intricate task of identifying and systematically
categorizing the various perspectives and opinions
expressed within the original text. In today's digital age,
social media platforms serve as a prolific source of data,
inundated with a relentless stream of status updates,
tweets, and content imbued with sentiments. Analysing
the sentiments conveyed by users in this vast reservoir of
data holds a pivotal role in comprehending the collective
sentiments of the user community, dissecting dialogues,
and aggregating viewpoints. This, in turn, can be
instrumental in shaping strategies for commerce,
conducting insightful political research, and gauging the
pulse of communal activities. Examining sentiments on
Twitter presents an increased difficulty because of the
frequency of spelling errors, casual language, icons, and
emojis. This research focuses on Twitter sentiment
analysis, with a specific emphasis on a particular user
account. The approach involves a combination of Python
programming and Machine Learning techniques. By
embarking on a comprehensive sentiment analysis
journey within a specific domain, the aim is to discern the
profound impact of that domain's data on sentiment
categorization. Furthermore, this paper introduces a
novel feature that enhances the organization of a user's
most recent tweets and their presentation through visual
aids such as graphs, charts, and word clouds. This
visualization approach empowers a more intuitive and
insightful exploration of the sentiments and trends
embedded within the user's Twitter activity, facilitating a
deeper understanding of their thoughts and emotions as
expressed through their digital interactions.
Keywords :
Twitter, Sentiment Analysis, Dataset, User Accounts, Machine Learning Algorithms, Tweets, Data Visualisation
Sentiment analysis is a crucial field that deals
with the intricate task of identifying and systematically
categorizing the various perspectives and opinions
expressed within the original text. In today's digital age,
social media platforms serve as a prolific source of data,
inundated with a relentless stream of status updates,
tweets, and content imbued with sentiments. Analysing
the sentiments conveyed by users in this vast reservoir of
data holds a pivotal role in comprehending the collective
sentiments of the user community, dissecting dialogues,
and aggregating viewpoints. This, in turn, can be
instrumental in shaping strategies for commerce,
conducting insightful political research, and gauging the
pulse of communal activities. Examining sentiments on
Twitter presents an increased difficulty because of the
frequency of spelling errors, casual language, icons, and
emojis. This research focuses on Twitter sentiment
analysis, with a specific emphasis on a particular user
account. The approach involves a combination of Python
programming and Machine Learning techniques. By
embarking on a comprehensive sentiment analysis
journey within a specific domain, the aim is to discern the
profound impact of that domain's data on sentiment
categorization. Furthermore, this paper introduces a
novel feature that enhances the organization of a user's
most recent tweets and their presentation through visual
aids such as graphs, charts, and word clouds. This
visualization approach empowers a more intuitive and
insightful exploration of the sentiments and trends
embedded within the user's Twitter activity, facilitating a
deeper understanding of their thoughts and emotions as
expressed through their digital interactions.
Keywords :
Twitter, Sentiment Analysis, Dataset, User Accounts, Machine Learning Algorithms, Tweets, Data Visualisation
