Character level embedding with deep convolutional neural network for text normalization of unstructured data for Twitter sentiment analysis

Paraphrase detection is an important task in text analytics with numerous applications such as plagiarism detection, duplicate question identification, and enhanced customer support helpdesks. Deep models have been proposed for representing and classifying paraphrases. These models, however, require large quantities of human-labeled data, which is expensive to obtain. In this work, we present a data augmentation strategy and a multi-cascaded model for improved paraphrase detection in short texts. Our data augmentation strategy considers the notions of paraphrases and non-paraphrases as binary relations over the set of texts. Subsequently, it uses graph theoretic concepts to efficiently generate additional paraphrase and non-paraphrase pairs in a sound manner. Our multi-cascaded model employs three supervised feature learners (cascades) based on CNN and LSTM networks with and without soft-attention. The learned features, together with hand-crafted linguistic features, are then forwarded to a discriminator network for final classification. Our model is both wide and deep and provides greater robustness across clean and noisy short texts. We evaluate our approach on three benchmark datasets and show that it produces a comparable or state-of-the-art performance on all three.• We present an efficient strategy for augmenting existing paraphrase and non-paraphrase annotations in a consistent manner. This strategy generates additional annotations and enhances the performance of the data-hungry deep learning models.• We develop a multi-cascaded learning model for robust paraphrase detection in both clean and noisy texts. This model incorporates multiple learned and linguistic features in a wide and deep discriminator network for paraphrase detection.• We address both clean and noisy texts in our presentation and show that the proposed model matches current best performances on benchmark datasets of both types.• We analyze the impact of various data augmentation steps and different components of the multicascaded model on paraphrase detection performance.

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“…4 Ferreira et al (2018) [44] 74.08 83. 1 Agarwal et al (2018) [4] 77.7 84.5 Arora and Kansal (2019) [45] 79.0 − Our model 78.3 84.8…”

Section: Msrp Datasetmentioning

confidence: 80%

A multi-cascaded model with data augmentation for enhanced paraphrase detection in short texts

Shakeel

Karim

Khan

2020

Information Processing & Management

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“…In this regard, an example of CNN combined with fuzzy logic called the Fuzzy Convolutional Neural Network (Nguyen, Kavuri & Lee, 2018) is noteworthy. The use of CNN in natural language processing is now a common topic and much research is being done in this area (Wehrmann et al, 2017;Gan et al, 2020;Arora & Kansal, 2019).…”

Section: Related Workmentioning

confidence: 99%

Persian sentiment analysis of an online store independent of pre-processing using convolutional neural network with fastText embeddings

Shumaly

Yazdinejad

Guo

2021

PeerJ Computer Science

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Sentiment analysis plays a key role in companies, especially stores, and increasing the accuracy in determining customers’ opinions about products assists to maintain their competitive conditions. We intend to analyze the users’ opinions on the website of the most immense online store in Iran; Digikala. However, the Persian language is unstructured which makes the pre-processing stage very difficult and it is the main problem of sentiment analysis in Persian. What exacerbates this problem is the lack of available libraries for Persian pre-processing, while most libraries focus on English. To tackle this, approximately 3 million reviews were gathered in Persian from the Digikala website using web-mining techniques, and the fastText method was used to create a word embedding. It was assumed that this would dramatically cut down on the need for text pre-processing through the skip-gram method considering the position of the words in the sentence and the words’ relations to each other. Another word embedding has been created using the TF-IDF in parallel with fastText to compare their performance. In addition, the results of the Convolutional Neural Network (CNN), BiLSTM, Logistic Regression, and Naïve Bayes models have been compared. As a significant result, we obtained 0.996 AUC and 0.956 F-score using fastText and CNN. In this article, not only has it been demonstrated to what extent it is possible to be independent of pre-processing but also the accuracy obtained is better than other researches done in Persian. Avoiding complex text preprocessing is also important for other languages since most text preprocessing algorithms have been developed for English and cannot be used for other languages. The created word embedding due to its high accuracy and independence of pre-processing has other applications in Persian besides sentiment analysis.

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“…Arora and Kansal [17] proposed an architecture which embeds the character level convolutional neural network (CNN) for performing sentiment analysis (SA) of unstructured data and thereby performs text normalization and classification of sentiments. This thereby helps in determining the actual polarity of the text message like whether the text indicates a positive, negative or neutral point of view.…”

Section: Deep Learningmentioning

confidence: 99%

Cooking Is Creating Emotion: A Study on Hinglish Sentiments of Youtube Cookery Channels Using Semi-Supervised Approach

Kaur

Kaushik

Sharma

2019

BDCC

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The success of Youtube has attracted a lot of users, which results in an increase of the number of comments present on Youtube channels. By analyzing those comments we could provide insight to the Youtubers that would help them to deliver better quality. Youtube is very popular in India. A majority of the population in India speak and write a mixture of two languages known as Hinglish for casual communication on social media. Our study focuses on the sentiment analysis of Hinglish comments on cookery channels. The unsupervised learning technique DBSCAN was employed in our work to find the different patterns in the comments data. We have modelled and evaluated both parametric and non-parametric learning algorithms. Logistic regression with the term frequency vectorizer gave 74.01% accuracy in Nisha Madulika’s dataset and 75.37% accuracy in Kabita’s Kitchen dataset. Each classifier is statistically tested in our study.

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Character level embedding with deep convolutional neural network for text normalization of unstructured data for Twitter sentiment analysis

Cited by 94 publications

References 35 publications

A multi-cascaded model with data augmentation for enhanced paraphrase detection in short texts

A multi-cascaded model with data augmentation for enhanced paraphrase detection in short texts

Persian sentiment analysis of an online store independent of pre-processing using convolutional neural network with fastText embeddings

Cooking Is Creating Emotion: A Study on Hinglish Sentiments of Youtube Cookery Channels Using Semi-Supervised Approach

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