Customer Opinion Summarization Based on Twitter Conversations

Othman, Rania; Belkaroui, Rami; Faiz, Rim

doi:10.1145/2912845.2912862

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…Kamps et al [6] determined semantic polarity of adjectives using a distance metric on WordNet. Othman et al [15] employed the lexicon SentiWordnet created by Baccianella et al [2] to compute tweet polarities for sentiment summarization in public conversations.…”

Section: Sentiment Analysismentioning

confidence: 99%

Deep Hybrid Neural Networks with Improved Weighted Word Embeddings for Sentiment Analysis

Othman¹,

Faiz²,

Abdelsadek³

et al. 2021

Advances in Intelligent Data Analysis XIX

Self Cite

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Deep learning models such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models have recently emerged as effective solutions to various NLP tasks with comparatively remarkable results. The CNN model efficiently extracts higher level features using convolutional layers and max-pooling layers while the LSTM model allows capturing long-term dependencies between word sequences. In this paper, we propose a hybrid CNN-LSTM model taking advantage of both models to overcome the sentiment analysis problem on Twitter data. We employ a multi-channel CNN that extracts local n-gram features in different sizes using several filters of different lengths. We also employ a weighted average word embeddings method which incorporates sentiment information in the continuous representation of words based on an adapted version of the delta TFIDF measure. These word representations will be the input for the CNN-LSTM model. Experiment results substantiate our intuition by reaching good macro average recall and accuracy scores beating several existing models as well as our same model using individual CNN and LSTM.

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Section: Sentiment Analysismentioning

confidence: 99%

Deep Hybrid Neural Networks with Improved Weighted Word Embeddings for Sentiment Analysis

Othman¹,

Faiz²,

Abdelsadek³

et al. 2021

Advances in Intelligent Data Analysis XIX

Self Cite

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show abstract

“…A lot of examples can be afforded for this type of summarization, especially with the growth of interest towards users opinions. For instance, the work of Othman et al [2016] is based on summarizing customer opinions through Twitter. Given a conversation of tweets, the method tries to effectively extract the different product features as well as the polarity of the conversation messages.…”

Section: Partialitymentioning

confidence: 99%

Automatic text summarization: What has been done and what has to be done

Aries,

Zegour,

Hidouci

2019

Preprint

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Summaries are important when it comes to process huge amounts of information. Their most important benefit is saving time, which we do not have much nowadays. Therefore, a summary must be short, representative and readable. Generating summaries automatically can be beneficial for humans, since it can save time and help selecting relevant documents. Automatic summarization and, in particular, Automatic text summarization (ATS) is not a new research field; It was known since the 50s. Since then, researchers have been active to find the perfect summarization method. In this article, we will discuss different works in automatic summarization, especially the recent ones. We will present some problems and limits which prevent works to move forward. Most of these challenges are much more related to the nature of processed languages. These challenges are interesting for academics and developers, as a path to follow in this field.

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“…This type of data contains spatial, textual, and temporal (STT) information. As a result, STT data analysis is becoming increasingly important [9] since it allows to extract new insights regarding customer satisfaction, user-generated content shared online, and brand reputation [27].…”

Section: Introductionmentioning

confidence: 99%