IRJET- Sentiment Analysis on Twitter Data using ML

International Research Journal of Engineering and Technology (IRJET) Volume: 07 Issue: 07 | July 2020 www.irjet.net

e-ISSN: 2395-0056 p-ISSN: 2395-0072

Sentiment Analysis on Twitter Data using ML Nihal G Bailur1,

Prof. Merin Meleet2

1RV18SIT13

Assistant Professor

Department of Information Science and Engineering

Department of Information Science and Engineering RV

College of Engineering

RV College of Engineering

Bengaluru, India

Bengaluru, India

------------------------------------------------------------------------------***--------------------------------------------------------------------------Abstract-Twitter provides affiliate companies a snappy and feasible way of addressing their views, businesses, managers and the public's feelings. Many features and methodologies have been asked about starting late with moving results in order to prepare inclination classifiers for Twitter datasets. A new technique is introduced to include semantics as further features in a sensation assessment readiness measures the relationship of the thinking agent with negative /positive ends. I also investigate a procedure based on sentimental assessments and find that the semanthetic features produce better recall and f score when requesting negative idea, and better accuracy with lower record and f in positive analyses characteristics. I therefore apply to manage foreseen feelings for three different Twitter datasets.

1. Introduction 1.1 Sentiment Analysis Estimation evaluation is the endeavor of finding the potential outcomes and relationship with individuals towards away from of plotting. Appraisals of individuals matter, and it confirmation's the dynamic philosophy of individuals. The crucial thing an individual does when the individual needs to purchase a thing on the web, is to see such an outlines and feelings, that individuals have made an enormous number out of individuals are utilizing social virtuoso objectives to give their doubts, evaluation and reveal about their a smidgen at a time lives. For instance, social exercises or any affirmation on things. Through the online frameworks give a sharp discussion where customers train and effect others besides, electronic life licenses to business that giving a stage to interface with their customers. The decent association can change the brief and dynamic of purchasers, for example, makes reference to that 87% of web clients are influenced in their buy and choice by client's examination. So that, if arrangement can locate a superior than normal pace quicker on what their client's figure, it would be constantly valuable to make to respond on condition and appear with a not all that horrible structure to battle their adversaries.so here we use idea assessment for clients so they can purchase the thing trust upon definite outcome. Online life has gotten more idea nowadays. Open and private inclination about a wide course of action of subjects are passed on and spread ceaselessly by systems for different online individual to singular correspondence. Twitter is one of the online life that is getting inevitability. Twitter offers affiliations an enthusiastic and astonishing way to deal with oversee research customers' perspectives toward the principal to accomplishment in the business organize.

1.2 Societal relevance Disappointment situated internet publicizing On-line business Voting educate applications Clarification concerning legislators' positions Real-world occasions observing Policy or government-guideline recommendations intelligent transportation frameworks 1.3 Introduction to sentiment analysis Essentially, it is the course toward picking if a hint of making is certain or negative. This is besides called the Polarity of the substance. As people, we can hoard content into helpful/antagonistic subliminally. For instance, the sentence "The adolescent had an amazing grin all completed", will more than likely give us a positive tendency. In layman's terms, we sort of show up at such end by separating the words and averaging out the positives and the negatives. For example, the words "astonishing" and "grin" will without a doubt be sure, while words like "the", "child" and "face" are unfathomably reasonable. Thusly, the general doubt of the sentence is in all probability going no uncertainty. An average use for this progression starts from its game-plan in the online frameworks organization space to find how individuals feel about

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International Research Journal of Engineering and Technology (IRJET) Volume: 07 Issue: 07 | July 2020 www.irjet.net

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express centers, especially through clients' assurance of-mouth in printed posts, or as for Twitter, their tweets.

1.4 Basic knowledge Despite the fact that Python is exceptionally associated with this smaller than expected venture, it isn't required to have a profound information in the language, as long as you have fundamental programming information.

1.5 Installed tools: For this program, we will require Python to be introduced on the PC. We will utilize the libraries twitter, nltk, re, csv, time, and json. You are probably going to need to introduce the initial two libraries. The rest previously accompany the Python translator. It doesn't damage to watch that they're cutting-edge however.

1.6 Informational collection parting idea: This is basic to completely comprehend the procedure pipeline. You just need to realize the contrast among Training and Test informational collections, and in what setting every one is utilized.

1.7 Essential RESTful API information This isn't critical; however it could help. We will utilize the Twitter API to a great extent in the code, making ordinary calls to the API and managing the JSON objects it returns. In the event that you need it, you can locate the official Twitter API documentation here.

2. Literature Survey Statistical Features-Based Real-Time Detection of Drifted Twitter Spam Spam has now turned into a critical issue for Twitter. The study focuses on the implementation of Twitter machine learning strategies and allows the use of tweet statistics. spam detection. We notice, however, that the statistical properties of spam tweets differ with the time in our labeled tweet collection; therefore, the performance of current classificatory based on machine learning differs. The problem is known as "Twitter Spam Drift." In the fight against this problem, we first analyze the statistical features of 1,000,000 spam tweets and 1,000,000 non-spam tweets Effect of Spam on Hash tag Recommendation for Tweets Spam tweets can affect the feature selection, formulation of algorithms and system assessments of many applications. However, the impact of Spam tweets has not been taken into account in most existing studies. This paper analyzes the impact of spam tweets on the Hashtag recommendation in the HSpam14 data set for hyperlinked tweets (i.e., URL tweets). HSpam14 is a 14-million tweet collection with spam and ham annotations (i.e. spam non-spam). In our experiments, the recommendation of "correct" hashtags for spam tweets is much easier than ham tweets, because spam tweets are almost duplicate.

HSpam14: A Collection of 14 Million Tweets for Hashtag- Oriented Spam Research By creating dynamic and virtual community for information aggregation for all Twitter users, Hashtag facilitates information spreading in Twitter. Since hashtags are an additional way for tweets to be accessed by users other than its own, it is for Spamming purposes (eg. hashtag hijacking) that hashtags are targeted, especially for the popular hashtags and the trending hashtags. While a lot was spent on e-mail / web spam fighting, limited studies on hashtag-oriented spam in tweets An Analysis of 14 Million Tweets on Hashtag-Oriented Spamming Twitter has become a popular information dissemination and collection platform over the years. However, Twitter's popularity has attracted not only legit but also spammers who use social graphics, popular keywords and malicious hashtags. This report presents a detailed analysis of the dataset HSpam14 containing 14million tweets on Twitter with spam and ham- labels (i.e. non-spam-labels). The main aim of this paper is to analyze various aspects of Twitter spam, which are useful both for tweeting and user spam detection, using hashtags, twitter content and user profiles.

Semi-Supervised Spam Detection in Twitter Stream In most Twitter spam detection techniques, users posting spam tweets are identified and blocked. This paper proposes a tweet- level, semi-controlled spam detection (S3D) frame. The framework proposed includes two principal modules: the real- time spam detection module and the batch-operating model update. The spam detection module comprises four lightweight sensors: I a domain detector blacklisted for tweets with blacklist URLs; (ii) a near-duplicate detector for

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tweets that are almost duplicates of confidently tweets; 3. Methodology

Fig-1: System Architecture Pre-preparing of the datasets 

Data COLLECTION: Sentiments as tweets assembled from Twitter/some different stages

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TOKENSIER:

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Filtrating the content of data: Here the contents are filtered into specific form

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Nouns/pronouns are removed from the collected data : In this nouns are removed from the dataset for better user experience

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Measures the power of any word i.e. is it utilized as an action word or descriptive word ?

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Remove slag word from the data

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Remove URL

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Remove HASTAG(#) and numbers.

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Negation: Very huge in nostalgic examination for the "not" can in like manner be used for positive as "not simply " … so there can be no disorder !!

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FEATURE EXTRACTION: In this part calculation of number of upper case words(+/-),number of hastags, number of emojis, number of unique character

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Information are put away in MySQL Information are taken by utilizing web scratching strategy

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Naive Bayes algorithm is used for sentiment analysis

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Created UI using flask

Feature Extraction: There are many particular properties in the pre-prepared dataset. We remove viewpoints from the handled dataset in the component extraction technique. Subsequently this perspective is used to record the positive and negative extremity in a sentence that is useful for assessment determination. More work has been done to better describe this characteristic using word presence instead of frequencies. The results were improved by using presence rather than frequencies.

Parts of Speech: Subjectiveness and sentiment can be seen in parts of speech such as adjectives, adverbs and some verb and substantive groups. Syntactic addiction patterns can be generated by parsing or addiction trees.

Words and phrases of opinion: Apart from certain words, certain sentences and idioms that convey sentiments can be used as characteristics.

Position of Terms: The position of a term in a text can influence how much the term changes the text's feeling.

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Negation: Denial is a major but hard to interpret feature. In general, the presence of negation modifies the polarity of opinion.

For example, I'm not happy with Syntax Syntax Syntactic patterns like collocation used by many researchers to learn subjectivity patterns.

Implementation Training: Supervised learning is an important technique for solving classification problems. Training the classifier makes it easier for future predictions for unknown data. Here we train the data and later we test the data.

Classification Naive Bayes: Here we use Naïve bayes classifier is used to classify the data..i.e, how many percentage of positive, negative &neutral words are occurred in the graph To train and classify using Naïve Bayes Machine Learning technique, we can use the Python NLTK library Platform Selection Windows operating system is chosen as the platform for the system. Windows platform is simple and relatively easier to use. There is also plenty of documentation for windows system. Also, windows being an open source operating system, eliminates the need to manipulate system data structure. As the project is done using python in PyCharm which is platform independent, there can be a smoother adaption of the system on to other Testing model In this sectionWe discuss the implementation of the model testing Trained data from the loaded model takes place in real world processing of the cell location. The models learn the positions and search the same points in the net frames through the testing process of different machine learning models.

4. Results These results go about as the underlying advance of our portrayal approach. We simply use the short-recorded features for both of these results. This suggests for the objective plan we have 3 features and for positive, negative and neutral For results we use the Naïve Bayes portrayal computation, since that is the figuring, we are using in our certified gathering approach at the underlying advance. I make a condition while enumerating the eventual outcomes of furthest point portrayal (which isolates among positive and negative, neutral classes) that solitary enthusiastic named tweets are used to process these results. I create a GUI using flask and we take datasets of railways, cinemas and others. These are the screenshots of the project

FIG-2: Registration Form

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Fig-3: Pie Chart 5. Conclusion

I have built up a model which performs opinion examination on Twitter information utilizing Machine Learning Strategy.

The model that was proposed in this exploration worked by utilizing Natural Language Tool Kit (NLTK) on the dataset containing tweets. Pack of words idea is utilized which contains both positive and negative and neutral words independently. The order was finished utilizing Naïve Bayes classifier by figuring the likelihood of new info information and the tweet with the most elevated worth is considered as either positive, neutral &negative. we picked a successful twitter highlight dataset which upgrades the adequacy and precision of the classifier. This model can additionally upgrade to any wanted level in the event that one needs to by joining more highlights in database References [1] C. Chen, J. Zhang, X. Chen, Y. Xiang, and W. Zhou, “6 million spam tweets: A large ground truth for timely twitter spam detection,” in 2015 IEEE International Conference on Communications (ICC), June 2015, pp. 7065–7070.

[2] A. Greig, “Twitter Overtakes Facebook as the Most Popular Social Network for Teens, According to Study, Daily Mail, accessed on Aug. 1, 2015, ” http://www.dailymail.co.uk/news/article-2475591/Twitter- overtakes-Facebook-popular social network-teens-according- study. Html, 2015, [Online].

[3] H. Tsukayama, “Twitter turns 7: Users send over 400 million tweets per day,” https://tinyurl.com/ybsaq7e7, 2013, [Online].

[4] F. Benevenuto, G. Magno, T. Rodrigues, and V. Almeida, “Detecting spammers on twitter,” in In Collaboration, Electronic messaging, Anti-Abuse and Spam Conference (CEAS, 2010.

[5] C. Pash., “The lure of Naked Hollywood Star Photos Sent the Internet into Meltdown in New Zealand, Bus. Insider, accessed on Aug. 1, 2015,” https://tinyurl.com/yc93ssj4, 2014, [Online].

[6] “BotMaker,” https://blog.twitter.com/engineering/en us/a/2014/ fighting-spam-with-botmaker.html, [Online].

[7] K. Thomas, C. Grier, D. Song, and V. Paxson, “Suspended accounts in retrospect: An analysis of twitter spam,” in Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, ser. IMC ’11. New York, NY, USA: ACM, 2011, pp. 243–258. [Online]. Available: http://doi.acm.org/10.1145/2068816.2068840 [8] T. Wu, S. Liu, J. Zhang, and Y. Xiang, “Twitter spam detection based on deep learning,” in Proceedings of the Australasian Computer Science Week Multiconference, ser. ACSW ’17.

New York, NY, USA: ACM, 2017, pp. 3:1–3:8. [Online]. Available: http://doi.acm.org/10.1145/3014812.3014815

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