Twitter Classification of Complaints
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© April 2023| IJIRT | Volume 9 Issue 11 | ISSN: 2349-6002
Twitter Classification of Complaints
Aniket Solanki1, Harsh Tripathi2, Harsheet Soni3, Kunal Singh4, Naina Kaushik5
1234
Student Computer Engineering, MCT’s Rajiv Gandhi Institute of Technology, Mumbai University
5
Professor, MCT’s Rajiv Gandhi Institute of Technology, Mumbai University
Abstract—This project addresses the problem of negative response (since twitter allows us to download
sentiment analysis in twitter; that is classifying tweets stream of geo-tagged tweets for particular locations. If
according to the sentiment expressed in them: positive, firms can get this information they can analyze the
negative or neutral. Twitter is an online micro-blogging reasons behind geographically differentiated response,
and social-networking platform which allows users to and so they can market their product in a more
write short status updates of maximum length 140 optimized manner by looking for appropriate solutions
characters. It is a rapidly expanding service with over like creating suitable market segments. Predicting the
200 million registered users out of which 100 million are results of popular political elections and polls is also
active users and half of them log on twitter on a daily an emerging application to sentiment analysis. One
basis - generating nearly 250 million tweets per day. Due
such study was conducted by Tumasjan et al. in
to this large amount of usage we hope to achieve a
Germany for predicting the outcome of federal
reflection of public sentiment by analysing the
elections in which concluded that twitter is a good
sentiments expressed in the tweets. Analysing the public
reflection of offline sentiment.
sentiment is important for many applications such as
firms trying to find out the response of their products in
the market, predicting political elections and predicting
socioeconomic phenomena like stock exchange. The aim
of this project is to develop a functional classifier for
accurate and automatic sentiment classification of an
unknown tweet stream.
Index Terms— Sentiments, Predicting.
I. INTRODUCTION
We have chosen to work with twitter since we feel it is
a better approximation of public sentiment as opposed
to conventional internet articles and web blogs. The
reason is that the amount of relevant data is much
larger for twitter, as compared to traditional blogging
sites. Moreover the response on twitter is more prompt
Fig 1: System Architecture
and also more general (since the number of users who
tweet is substantially more than those who write web II. LITERATURE REVIEW
blogs on a daily basis).
A. Literature Review
Sentiment analysis of public is highly critical in Due to the enormous amount of data generated on
macro-scale socioeconomic phenomena like social media networks, Twitter sentiment analysis is a
predicting the stock market rate of a particular firm. burgeoning field of study. An overview of the most
This could be done by analysing overall public recent studies on Twitter sentiment analysis is
sentiment towards that firm with respect to time and provided in this review of the literature.
using economics tools for finding the correlation
between public sentiment and the firm’s stock market 1)"Deep Learning Techniques for Sentiment Analysis
value. Firms can also estimate how well their product on Twitter Data: A Review" by R. Selvi et al. (2021).
is responding in the market, which areas of the market Deep learning techniques for sentiment analysis on
is it having a favourable response and in which a Twitter are examined in detail in this work. The
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© April 2023| IJIRT | Volume 9 Issue 11 | ISSN: 2349-6002
authors explore several pre-processing and feature 2) Lexalytics: A text analytics software provider called
extraction strategies in addition to comparing various Lexalytics provides sentiment analysis solutions,
deep learning architectures. They also emphasize the including a Twitter sentiment analysis solution. It
difficulties with Twitter sentiment analysis and offer analyses text and identifies sentiment using machine
suggestions for further study. learning and natural language processing techniques.
2) "Sentiment Analysis of Twitter Data: A Complete Several languages of text can be analysed using
Study," by P. Pakhare et al. (2021), offers a thorough Lexalytics, which can also be tailored to certain
analysis of numerous methods such as lexicon-based, fields.Text is given a subjectivity score and a
machine learning-based, and deep learning-based sentiment polarity score by Lexalytics. The emotion of
techniques for sentiment analysis on Twitter data. The the text is indicated by the polarity score, which is a
authors examine the difficulties sarcasm and irony float value between -1 and 1. (negative to positive).
provide and make recommendations for future 3) CoreNLP: A sentiment analysis module is part of
research topics. the Java-based natural language processing toolbox
3) First most recent techniques and uses of sentiment known as Stanford CoreNLP. It analyses text using
analysis on Twitter are covered by M. I. Ali et al. in deep learning techniques and assigns a sentiment
their paper "Twitter Sentiment Analysis: A Survey of score. Stanford CoreNLP can be tailored to particular
Techniques and Applications" from 2020. They fields and can analyse text in a variety of
contrast various strategies, including lexicon-based, languages.Text is given a sentiment polarity score and
machine learning-based, and deep learning-based a subjectivity score using Stanford CoreNLP. The
ones, and offer suggestions for further research. emotion of the text is indicated by the polarity score,
4) In "Sentiment Analysis on Twitter Data: A which is a float value.
Review," R. Kumar et al. (2020), they concentrate on 4) Vader Sentiment Analysis: The rule-based
the various methods for pre-processing, feature sentiment analysis program Vader (Valence Aware
extraction, and sentiment classification. The authors Dictionary and Sentiment Reasoner) employs a
talk about the difficulties in performing sentiment dictionary of words and phrases to assign a sentiment
analysis on Twitter data and make recommendations score to text. The lexicon includes positive and
for future study areas. negative sentiment scores for words and phrases and
is based on human-labeled data. Moreover, Vader
The informality of tweets and the usage of irony and considers negations, intensifiers, and other textual
sarcasm, according to the literature, make Twitter elements that may have an impact on emotion.
sentiment analysis a difficult endeavour. Whereas Vader has demonstrated strong performance on
recent developments in deep learning algorithms have Twitter and other social media metrics. It features a
yielded encouraging results, future research should straightforward API for text analysis and is available
concentrate on enhancing the precision of sentiment as a Python package.
analysis using Twitter data. 5) TextBlob: It is a Python package that offers a
straightforward API for carrying out typical natural
B. Survey of Existing Systems language processing operations, such as sentiment
1) IBM Watson: A sentiment analysis service is part analysis. It categorises text as either positive, negative,
of IBM Watson, a collection of AI-powered products. or neutral using a Naive Bayes classifier. The classifier
It analyses text using machine learning techniques and can be applied to different domains and was trained on
assigns a sentiment score. IBM Watson may be a labelled dataset of movie reviews.TextBlob also
tailored to particular domains and can evaluate text in assigns a subjectivity and sentiment polarity score to
a variety of languages.Moreover, IBM Watson offers each piece of text. The emotion of the text is indicated
a subjectivity score and a sentiment polarity score for by the polarity score, which is a float value between -
text. The emotion of the text is indicated by the 1 and 1. (negative to positive).
polarity score, which is a float value between -1 and 1.
(negative to positive). The subjectivity score, which C. linitation of Existing Systems/Research gap
ranges from 0 to 1, reflects how subjective 1) Sampling bias: Since Twitter users may not
(opinionated) or objective the text is (factual) represent the entire population, the opinions posted
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there might not be reflective of the general public. Data preprocessing and cleaning: After the data has
Compared to the average population, Twitter users been gathered, it must be cleaned and preprocessed to
tend to be younger, more urban, and politically active. weed out extraneous information and make it ready for
2) Limited context: Because Twitter tweets can only analysis. There are multiple steps in this:
be 280 characters long, they frequently lack the a. Eliminating duplicate tweets: It's critical to get rid
background information required to fully comprehend of duplicate tweets before sentiment analysis because
the mood being expressed. An seeming negative tweet, they can bias the results.
for instance, can actually be ironic or satirical. b. Retweets can potentially bias the results of
3) Slang and colloquial language: Twitter users sentiment analysis because they do not reflect the
frequently employ colloquial language, which can be original sentiment.
challenging for sentiment analysis algorithms to c. Removing spam: Spam tweets, which can be found
understand. This can result in incorrect sentiment via automatic or manual approaches, can also skew the
analysis findings. results.
4) Irony and sarcasm: Twitter users frequently employ d. Eliminating non-textual content: To draw attention
irony and sarcasm to convey their thoughts, which to text-only content, non-textual content such as
sentiment analysis algorithms may find challenging to images, videos, and other media might be eliminated.
identify. Even though a tweet seems to be harsh, it e. Text normalisation, often known as case-insensitive
could also be positive. text or punctuation removal, is the process of
5) Sentiment polarity: Text is often categorised as transforming text to a standard format.
either positive, negative, or neutral by algorithms that
analyse sentiment. But sentiment polarity is frequently Classification of sentiment: Positive, negative, or
more nuanced than this and can change based on the neutral sentiment is assigned to each tweet based on
situation and the speaker. algorithms that analyse sentiment. There are various
6) Minimal training data: To learn how to classify text, methods for sentiment analysis, such as:
sentiment analysis algorithms need training data. a. Rule-based strategies: These strategies utilise a set
Nevertheless, there may not be enough training data of pre-established rules to assess the tone of a tweet
for Twitter sentiment analysis, which could result in based on its keywords or other characteristics.
unreliable findings. b. Machine learning techniques: Using machine
7) Emojis and emoticons: Twitter users frequently learning techniques, a model is trained on a labelled
utilise emojis and emoticons to convey emotion, dataset to discover how to categorise text according to
which can be challenging for algorithms that analyse its sentiment.
sentiment. Depending on the situation, the same emoji c. Hybrid methods: To increase accuracy, hybrid
can imply something entirely different. approaches integrate rule-based and machine learning
8) Tweets in multiple languages are possible on techniques.
Twitter because it is a multilingual platform. But, it's
possible that algorithms for sentiment analysis won't Analyzing and visualising the results is the last step in
be able to effectively interpret tweets in languages the Twitter sentiment analysis process. In order to do
they haven't been trained on. this, it may be necessary to discover trends and
patterns in the data, identify significant influencers or
III. METHODOLOGY
subjects, and create graphs and charts to represent
Data Collection:Data gathering is the initial step in the sentiment across time.
sentiment analysis process for Twitter. The Twitter
API, third-party programmes like Tweepy, or for-
profit services like Gnip or DataSift can all be used for
this. The information may be gathered in real-time or
over a predetermined period of time, and it may
comprise tweets on a certain subject or hashtag, as well
as tweets from particular individuals or places.
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© April 2023| IJIRT | Volume 9 Issue 11 | ISSN: 2349-6002
—Fig 4:Live Tweet Sentiment Analysis of UEFA
—Fig 2: System Architecture
Overall, there are a number of processes in the
approach for Twitter sentiment analysis, including
data collection, cleaning and preprocessing, sentiment
classification, sentiment aggregation, and
visualisation and analysis. Depending on the study
issue and the objectives of the analysis, many
approaches may be employe.
IV.RESULTS
—Fig 4: Entered Tweet Sentimental Analysis
of UEFA
V. CONCLUSION
In this paper we conclude that the proposed twitter
sentimental analysis addresses the critical issues of
getting the sentiments of a human from their text.The
use of tweepy api of python helps to fetch live data from
the twitter api and sentiment can be used for predictive
analysis: Twitter sentiment analysis can be used to
predict future trends or events. By analyzing the
—Fig 3: Home Page
sentiment of tweets, researchers and businesses can
gain insight into the likelihood of a particular event
occurring or how consumers may react to a new
product or service. Machine learning algorithms can be
used to improve the accuracy of Twitter sentiment
analysis. By training the algorithms on large datasets,
they can learn to identify patterns in language and
sentiment that can help to improve the accuracy of the
analysis.
ACKNOWLEDGMENT
We wish to express our sincere gratitude to Dr. Sanjay
U. Bokade, Principal and Prof. S. P. Khachane, Head
of Department of Computer Engineering at MCT's
—Fig 3:Tweet Sentiment Analysis
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Rajiv Gandhi Institute of Technology, for providing us Fukuoka, Japan, Cyber Science and Technology
with the opportunity to work on our project, Congress, 2019
"Crowdfunding Platform Using Blockchain." This [8] M. Gamon, “Sentiment classification on customer
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colleagues and friends who helped us in completing Conference on Computational Linguistics, pp.
this project successfully. 841–847, 2004
[9] S. A. El Rahman, F. A. AlOtaibi, and W. A.
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