A Survey on NLP based Text Summarization for Summarizing Product Reviews

Boorugu, Ravali; Ramesh, G.

doi:10.1109/icirca48905.2020.9183355

Cited by 55 publications

(29 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another critical issue is the recognition of themes in a dialogue between a system and an individual. Accurate recognition of topics by a dialogue system requires a well-designed language comprehension system [ 13 , 14 ]. To design such a system, the syntax, i.e., the relations between the verbs and the structure of the phrases, the semantics, i.e., the reference, and the pragmatics, i.e., the analysis of the dialogues of information exchange of communication actions, must be considered.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods

Tang

2022

Applied Bionics and Biomechanics

View full text Add to dashboard Cite

Most of the sophisticated attacks in the modern age of cybercrime are based, among other things, on specialized phishing campaigns. A challenge in identifying phishing campaigns is defining a classification of patterns that can be generalized and used in different areas and campaigns of a different nature. Although efforts have been made to establish a general labeling scheme in their classification, there is still limited data labeled in such a format. The usual approaches are based on feature engineering to correctly identify phishing campaigns, exporting lexical, syntactic, and semantic features, e.g., previous phrases. In this context, the most recent approaches have taken advantage of modern neural network architectures to record hidden information at the phrase and text levels, e.g., Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). However, these models lose semantic information related to the specific problem, resulting in a variation in their performance, depending on the different data sets and the corresponding standards used for labeling. In this paper, we propose to extend word embeddings with word vectors that indicate the semantic similarity of each word with each phishing campaigns template tag. These embedded keywords are calculated based on semantic subfields corresponding to each phishing campaign tag, constructed based on the automatic extraction of keywords representing these tags. Combining general word integrations with vectors is calculated based on word similarity using a set of sequential Kalman filters, which can then power any neural architecture such as LSTM or CNN to predict each phishing campaign. Our experiments use a data indicator to evaluate our approach and achieve remarkable results that reinforce the state-of-the-art.

show abstract

Section: Introductionmentioning

confidence: 99%

A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods

Tang

2022

Applied Bionics and Biomechanics

View full text Add to dashboard Cite

show abstract

“…Text summarization can condense elaborated reviews into short sentences conveying the same idea and according to a research, combining the seq2seq model with the LSTM and attention mechanism can be effective for text summarizing. Their model uses multiple types of text summerization techniques based on input types, output types and the purposes [2]. Stock markets play a vital role in the economy but predicting and analysing it is not an easy task as a lot of factors are involved with it.…”

Section: Related Workmentioning

confidence: 99%

Product Market Demand Analysis Using NLP in Banglish Text with Sentiment Analysis and Named Entity Recognition

Hossain¹,

Nayla²,

Rasel³

2022

Preprint

View full text Add to dashboard Cite

Product market demand analysis plays a significant role for originating business strategies due to its noticeable impact on the competitive business field. Furthermore, there are roughly 228 million native Bengali speakers, the majority of whom use Banglish text to interact with one another on social media. Consumers are buying and evaluating items on social media with Banglish text as social media emerges as an online marketplace for entrepreneurs. People use social media to find preferred smartphone brands and models by sharing their positive and bad experiences with them. For this reason, our goal is to gather Banglish text data and use sentiment analysis and named entity identification to assess Bangladeshi market demand for smartphones in order to determine the most popular smartphones by gender. We scraped product related data from social media with instant data scrapers and crawled data from Wikipedia and other sites for product information with python web scrapers. Using Python's Pandas and Seaborn libraries, the raw data is filtered using NLP methods. To train our datasets for named entity recognition, we utilized Spacey's custom NER model, Amazon Comprehend Custom NER. A tensorflow sequential model was deployed with parameter tweaking for sentiment analysis. Meanwhile, we used the Google Cloud Translation API to estimate the gender of the reviewers using the BanglaLinga library. In this article, we use natural language processing (NLP) approaches and several machine learning models to identify the most in-demand items and services in the Bangladeshi market. Our model has an accuracy of 87.99% in Spacy Custom Named Entity recognition, 95.51% in Amazon Comprehend Custom NER, and 87.02% in the Sequential model for demand analysis. After Spacy's study, we were able to manage 80% of mistakes related to misspelled words using a mix of Levenshtein distance and ratio algorithms.

show abstract

“…There are several ways to classify text summarization. Of relevance here are the types of text summarization based on type of type of output [28]:…”

Section: Text Summarizationmentioning

confidence: 99%

“…These sentences are generated by learning from the input text body and forming new sentences that convey the same meaning and is seen in Figure 4 below. A procedure using seq2seq model with the LSTM and attention mechanism has been found to provide the highest accuracy [28]. Any kind of sequential information is a good candidate for the application of seq2seq model which consists of an encoder and decoder as its two main components as seen in Figure 5.…”

Section: Text Summarizationmentioning

confidence: 99%

Identification of Aphasia using Natural Language Processing

Rao¹,

S²

2021

JUSST

View full text Add to dashboard Cite

Aphasia is a neurological disorder of language that precludes a person’s ability to speak, understand, read or write in any language. By virtue of this disorder being inextricably connected to language, there is a vast potential for the application of Natural Language Processing (NLP) for the diagnosis of the disorder. This paper surveys the automated machine-learning-based classification methodologies followed by an attempt to discuss a potential way in which an NLP-backed methodology could be implemented along with its accompanying challenges. It is seen that the need for standardized technology-based diagnostic solutions necessitates the exploration of such a methodology.

show abstract

A Survey on NLP based Text Summarization for Summarizing Product Reviews

Cited by 55 publications

References 4 publications

A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods

A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods

Product Market Demand Analysis Using NLP in Banglish Text with Sentiment Analysis and Named Entity Recognition

Identification of Aphasia using Natural Language Processing

Contact Info

Product

Resources

About