Review on Natural Language Processing Trends and Techniques Using NLTK

Yogish, Deepa; Manjunath, T N; Hegadi, Ravindra S.

doi:10.1007/978-981-13-9187-3_53

Cited by 36 publications

(17 citation statements)

References 5 publications

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“…We have followed the widely used data analysis steps for Natural Language Processing for our research dataset, using Python (Python, 2014) as a data science tool for fast analytics and Tableau (Tableau, 2009) to display the results (Deepa, Manjunath, & Ravindra, 2019), (Subramanian, 2019). First, we have preprocessed our dataset in terms of text cleaning, translations, and lemmatization, and second, we have counted the frequencies of all words' occurrences from the posts and comments analysed.…”

Section: Methodsmentioning

confidence: 99%

The Rebellious Social Network Reaction to COVID-19

Cioban

Vîntoiu

2020

Studia Universitatis Babes-Bolyai Sociologia

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Gathering social media content and analysing the heavy and unstructured text coming from posts, comments and reactions can come as a powerful tool in understanding how people react to the information they receive. In this article we present the results from a social media analysis of 10771 headlines, with their subsequent text bodies and comments posted in a subreddit destined for Romanians during the state of emergency declared in Romania, from March 16 to May 15, 2020. Our objective was to model the main topics debated by this targeted population of people that tend to use Reddit to discuss current issues and to identify the sentiment polarity towards these topics. As expected, Romanians are mostly concerned with their social condition in the context of the pandemic caused by CoVID-19, as our research has revealed a word frequency for the term “Coronavirus” prominently higher than any other preferred term. However, the analysis brings up a surprising turnaround as the overall sentiment of the text posted in this dataset is predominantly neutral with a higher frequency of positive posts compared to the negative ones. This was unforeseen by our initial expectations: a natural tendency to more negative posts than positive considering the context of the chosen study period. Moreover, when compared to the time series of the CoVID-19 infections and caused deaths in Romania, spikes of extremely high or low mean sentiment scores per day can be correlated to the fluctuations of the declared cases. Not only does this bring us closer to understanding the social impact of CoVID-19 in the current context, but the outcome of this analysis can be easily extrapolated for further investigations upon other social networking tools or for more in-depth analysis on our studied corpus.

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Section: Methodsmentioning

confidence: 99%

The Rebellious Social Network Reaction to COVID-19

Cioban

Vîntoiu

2020

Studia Universitatis Babes-Bolyai Sociologia

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“…Various ML algorithms were also used to classify various Arabic languages documents with or without various preprocessing techniques and it were revealed that classification accuracy gives better results with preprocessing techniques [24].…”

Section: Different Preprocessing Techniques Provide Different Classif...mentioning

confidence: 99%

Improvisation in opinion mining using data preprocessing techniques based on consumer’s review

2023

IJATEE

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“…Within the widely studied field of NLP, information extraction is a possible application [21]. The goal of information extraction is to extract structured information from (semi)-unstructured documents.…”

Section: Document Segmentationmentioning

confidence: 99%

Automatically Extracting Insurance Contract Knowledge Using NLP

Goossens

Berth

Decoene

et al. 2022

Business Information Systems Workshops

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Vanbreda Risk & Benefits, a large Belgian insurance broker and risk consultant, allocates a substantial amount of time and resources to answer contract related questions from customers. This requires employees to manually search the relevant parameters in the contracts. In this paper, a solution is proposed and evaluated that automatically extracts insurance parameters from contracts using regular expressions and Natural Language Processing. While Natural Language Processing has been used in insurance for optimising premiums, detecting fraudulent claims, or underwriting, limited work has been done regarding parameter extraction. The proposed solution has been developed on 127 different contracts and two different contract types in terms of accuracy and time-performance. Moreover, the automatic parameter extraction has been compared to manual parameter extraction. We conclude that automatic parameter extraction using regular expressions achieves better accuracy than manual extraction on top of being significantly faster, allowing Vanbreda Risk & Benefits to invest more time into providing better customer service.

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Review on Natural Language Processing Trends and Techniques Using NLTK

Cited by 36 publications

References 5 publications

The Rebellious Social Network Reaction to COVID-19

The Rebellious Social Network Reaction to COVID-19

Improvisation in opinion mining using data preprocessing techniques based on consumer’s review

Automatically Extracting Insurance Contract Knowledge Using NLP

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