Natural Language Processing based Abstractive Text Summarization of Reviews

Shah, Janice; Sagathiya, Manasvi; Redij, Kareena; Hole, Varsha

doi:10.1109/icesc48915.2020.9155759

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…A framework is proposed [8] for removing formal semantic information from unstructured text utilizing an installed CNL. Sentimental Analysis is done on the pre-processed text reviews [9], following a sequence-to-sequence encoderdecoder with an attention layer is used for summarization method, preserving the semantics of the reviews. Various algorithms and methods are used to build text summarization tools [10] and these methods, in individual and together give different types of summaries.…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning based Automated Essay Summarization

Rohith

Srinivas

Deepika

et al. 2023

IJRITCC

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The human evaluation of essays has become a very time-consuming process as the number of schools and universities has grown. The available software entities are unable to assess the sentiment associated with essays. Thus, we propose a model using Natural Language Processing to assess the essay based on both grammar and sentiment associated with the essay by using linear regression and ULMFiT (Universal Language Model Fine-tuning for Text Classification) models. Evaluation of essay is done in two parts. Part one is on essay grading with respect to grammar with maximum 12 and minimum 0 grade points and in part two score of 0/1 for sentiment analysis with 0 being negative and 1 being positive. The model can be used to score the essay and discard any essay with a score less than a specified value or specified sentiment score.

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Section: Related Workmentioning

confidence: 99%

Transfer Learning based Automated Essay Summarization

Rohith

Srinivas

Deepika

et al. 2023

IJRITCC

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“…To perform the Sentiment Analysis, we employ two open-source algorithms: IBM "Alchemy Language" (which at the time of writing has been integrated into the Watson line of products) 3 and IBM Watson "Tone Analyzer. 4 Both algorithms use Machine Learning approaches to identify sentiments, and have been widely applied to text sources including customer reviews (Gao et al 2015;Shah et al 2020) and social media posts (Cao et al 2018;Jussila and Madhala 2019).…”

Section: Sentiment Towards Cars Over Timementioning

confidence: 99%

Sentiment analysis of popular-music references to automobiles, 1950s to 2010s

Vine

Bengel

et al. 2021

Transportation

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In recent years, there has been a scholarly debate regarding the decrease in automobile-related mobility indicators (car ownership, driving license holding, VMT, etc.). Broadly speaking, two theories have been put forward to explain this trend: (1) economic factors whose impacts are well-understood in principle, but whose occurrence among young adults as a demographic sub-group had been overlooked, and (2) less well-understood shifts in cultural mores, values and sentiment towards the automobile. This second theory is devilishly difficult to study, due primarily to limitations in standard data resources such as the National Household Travel Survey and international peer datasets. In this study we first compiled a database of lyrics to popular music songs from 1956 to 2015 (defined by inclusion in the annual “top 40”), and subsequently identified references to automobiles within this corpus. We then evaluated whether there is support for theory #2 above within popular music, by looking at changes from the 1950s to the 2010s. We demonstrate that the frequency of references to automobility tended for many years to increase over time, however there has more recently been a decline after the late 2000s (decade). In terms of the sentiment of popular music lyrics that reference automobiles, our results are mixed as to whether the references are becoming increasingly positive or negative (machine analysis suggests increasing negativity, while human analysis did not find a significant association), however a consistent observation is that sentiment of automobile references have over time become more positive relative to sentiment of song lyrics overall. We also show that sentiment towards automobile references differs systematically by genre, e.g. automobile references within ‘Rock’ lyrics are in general more negative than similar references to cars in other music genres). The data generated on this project have been archived and made available open access for use by future researchers; details are in the full paper.

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