A method to extract essential keywords from a tweet using NLP tools

Weerasooriya, Tharindu Cyril; Perera, Nandula; Liyanage, S.R.

doi:10.1109/icter.2016.7829895

Cited by 17 publications

(8 citation statements)

References 7 publications

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“…PoS taggers may be used for this task which is available in NLTK or Spacy. Researches most commonly use Stanford PoS-tagger (Weerasooriya et al 2016).…”

Section: Feature Extractionmentioning

confidence: 99%

A survey on sentiment analysis methods, applications, and challenges

2022

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The rapid growth of Internet-based applications, such as social media platforms and blogs, has resulted in comments and reviews concerning day-to-day activities. Sentiment analysis is the process of gathering and analyzing people's opinions, thoughts, and impressions regarding various topics, products, subjects, and services. People's opinions can be beneficial to corporations, governments, and individuals for collecting information and making decisions based on opinion. However, the sentiment analysis and evaluation procedure face numerous challenges. These challenges create impediments to accurately interpreting sentiments and determining the appropriate sentiment polarity. Sentiment analysis identifies and extracts subjective information from the text using natural language processing and text mining. This article discusses a complete overview of the method for completing this task as well as the applications of sentiment analysis. Then, it evaluates, compares, and investigates the approaches used to gain a comprehensive understanding of their advantages and disadvantages. Finally, the challenges of sentiment analysis are examined in order to define future directions.

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“…PoS taggers may be used for this task which is available in NLTK or Spacy. Researches most commonly use Stanford PoS-tagger (Weerasooriya et al 2016).…”

Section: Feature Extractionmentioning

confidence: 99%

A survey on sentiment analysis methods, applications, and challenges

2022

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“…For instance, Felbo et al (2017) developed an emoji prediction model for tweets. Weerasooriya et al (2016) discussed how to extract essential keywords from a tweet using NLP tools. Cohn et al (2019) attempted to understand the use of emojis from a grammatical perspective, seeking to determine the parts-of-speech of emoji occurrences in a sentence or tweet.…”

Section: Related Workmentioning

confidence: 99%

Assessing Emoji Use in Modern Text Processing Tools

Shoeb¹,

Melo²

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Emojis have become ubiquitous in digital communication, due to their visual appeal as well as their ability to vividly convey human emotion, among other factors. This also leads to an increased need for systems and tools to operate on text containing emojis. In this study, we assess this support by considering test sets of tweets with emojis, based on which we perform a series of experiments investigating the ability of prominent NLP and text processing tools to adequately process them. In particular, we consider tokenization, part-of-speech tagging, dependency parsing, as well as sentiment analysis. Our findings show that many systems still have notable shortcomings when operating on text containing emojis.

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“…People are using new methods to perform operations and the inputs and outputs are stored with time on these large sized databases. Data mining is a KDD based technology that examines and reveals the important information from the raw data collected in large databases [1]. Cluster analysis is known as an important method for data mining.…”

Section: Introductionmentioning

confidence: 99%

Logistic Regression Method for Sarcasm Detection of Text Data

Gupta¹

2019

IJCA

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The prediction analysis is approach which can predict future possibilities. This research work is based on the sarcasm detection from the text data. In the previous time SVM classification is applied for the sarcasm detection. The SVM classifier classifies data based on the hyper plane which give low accuracy. To improve accuracy for sarcasm detection logistic regression is applied in this work. The existing and proposed techniques are implemented in python and results are analyzed in terms of accuracy, execution time. The proposed approach has high accuracy and low execution time as compared to SVM classifier for sarcasm detection

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A method to extract essential keywords from a tweet using NLP tools

Cited by 17 publications

References 7 publications

A survey on sentiment analysis methods, applications, and challenges

A survey on sentiment analysis methods, applications, and challenges

Assessing Emoji Use in Modern Text Processing Tools

Logistic Regression Method for Sarcasm Detection of Text Data

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