Sentimental Short Sentences Classification by Using CNN Deep Learning Model with Fine Tuned Word2Vec

Sharma, Amit; Chaurasia, Sandeep; Srivastava, Devesh Kumar

doi:10.1016/j.procs.2020.03.416

Cited by 76 publications

(48 citation statements)

References 7 publications

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“…Recently, vector representations of documents or sentences are used as input features into supervised machine learning classifiers. Techniques used to build the vector representations include, TF‐IDF, 11,14 N‐grams 11,19 and Word2Vec models 15,20 among others. Chug, Gupta and Ahuja 11 analyzed the impact of two feature extraction techniques; TF‐IDF word level and N‐Gram on SS‐Tweet dataset of sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

“…In their work, 20 word vectors were generated from pre‐trained Word2Vec model and CNN layer was used to extract better features for short sentences categorization. Multiple channels pattern is applied for text processing in which one channel could be the sequence of words, another channel the sequence of corresponding POS tags, and the third one the shape of the word.…”

Section: Related Workmentioning

confidence: 99%

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Lexicon‐pointed hybrid N‐gram Features Extraction Model (LeNFEM) for sentence level sentiment analysis

Mutinda

Mwangi

Okeyo

2021

Engineering Reports

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Sentiment analysis of social media textual posts can provide information and knowledge that is applicable in social settings, business intelligence, evaluation of citizens' opinions in governance, and in mood triggered devices in the Internet of Things. Feature extraction and selection is a key determinant of accuracy and computational cost of machine learning models for such analysis. Most feature extraction and selection techniques utilize bag of words, N‐grams, and frequency‐based algorithms especially Term Frequency‐Inverse Document Frequency. However, these approaches do not consider relationships between words, they ignore words' characteristics and they suffer high feature dimensionality. In this paper we propose and evaluate a feature extraction and selection approach that utilizes a fixed hybrid N‐gram window for feature extraction and minimum redundancy maximum relevance feature selection algorithm for sentence level sentiment analysis. The approach improves the existing features extraction techniques, specifically the N‐gram by generating a hybrid vector from words, Part of Speech (POS) tags, and word semantic orientation. The vector is extracted by using a static trigram window identified by a lexicon where a sentiment word appears in a sentence. A blend of the words, POS tags, and the sentiment orientations of the static trigram are used to build the feature vector. The optimal features from the vector are then selected using minimum redundancy maximum relevance (MRMR) algorithm. Experiments were carried out using the public Yelp dataset to compare the performance of the proposed model and existing feature extraction models (BOW, normal N‐grams and lexicon‐based bag of words semantic orientations). Using supervised machine learning classifiers the experimental results showed that the proposed model had the highest F‐measure (88.64%) compared to the highest (83.55%) from baseline approaches. Wilcoxon test carried out ascertained that the proposed approach performed significantly better than the baseline approaches. Comparative performance analysis with other datasets further affirmed that the proposed approach is generalizable.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Lexicon‐pointed hybrid N‐gram Features Extraction Model (LeNFEM) for sentence level sentiment analysis

Mutinda

Mwangi

Okeyo

2021

Engineering Reports

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show abstract

“…Now slide to size k of the kernel to input all the words by multiplying the input vector by each kernel value and also by the number of overlapping values. Between the set of input insertion vectors in a given window and the weight vector u, there is a point product, where the non-linear activation function g often follows [31].…”

Section: Cnn Modelmentioning

confidence: 99%

Comparative Analysis of Performance between Multimodal Implementation of Chatbot Based on News Classification Data Using Categories

2021

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In the modern era, the implementation of chatbot can be used in various fields of science. This research will focus on the application of sentence classification using the News Aggregator Dataset that is used to test the model against the categories determined to create the chatbot program. The results of the chatbot program trial by multimodal implementation applied four models (GRU, Bi-GRU, 1D CNN, 1D CNN Transpose) with six variations of parameters to produce the best results from the entire trial. The best test results from this research for the chatbot program using the 1D CNN Transpose model are the best models with detailed characteristics in this research, which produces an accuracy value of 0.9919. The test results on both types of chatbot are expected to produce sentence prediction results and precise and accurate detection results. The stages in making the program are explained in detail; therefore, it is hoped that program users can understand not only how to use the program by entering an input and receiving program output results that are explained in more detail in each sub-topic of this study.

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“…This method generates a vocabulary from the input words and then learns the word vectors via backpropagation and stochastic gradient descent. 14 To visualize the ethnic relationships recorded in the Hetu Dangse catalog, we input the first 300 words of the word vector into the trained word2vec model and performed dimensionality reduction to realize a planar graph. To understand the structure of the data intuitively, we used the t-SNE algorithm to reduce the dimensions of the word vector.…”

Section: Word Vector Conversion Of Text Catalog Datamentioning

confidence: 99%

Text Analysis and Visualization Research on the Hetu Dangse During the Qing Dynasty of China

Wang

Jing-yu

et al. 2021

ITAL

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In traditional historical research, interpreting historical documents subjectively and manually causes problems such as one-sided understanding, selective analysis, and one-way knowledge connection. In this study, we aim to use machine learning to automatically analyze and explore historical documents from a text analysis and visualization perspective. This technology solves the problem of large-scale historical data analysis that is difficult for humans to read and intuitively understand. In this study, we use the historical documents of the Qing Dynasty Hetu Dangse,preserved in the Archives of Liaoning Province, as data analysis samples. China’s Hetu Dangse is the largest Qing Dynasty thematic archive with Manchu and Chinese characters in the world. Through word frequency analysis, correlation analysis, co-word clustering, word2vec model, and SVM (Support Vector Machines) algorithms, we visualize historical documents, reveal the relationships between functions of the government departments in the Shengjing area of the Qing Dynasty, achieve the automatic classification of historical archives, improve the efficient use of historical materials as well as build connections between historical knowledge. Through this, archivists can be guided practically in historical materials’ management and compilation.

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Sentimental Short Sentences Classification by Using CNN Deep Learning Model with Fine Tuned Word2Vec

Cited by 76 publications

References 7 publications

Lexicon‐pointed hybrid N‐gram Features Extraction Model (LeNFEM) for sentence level sentiment analysis

Lexicon‐pointed hybrid N‐gram Features Extraction Model (LeNFEM) for sentence level sentiment analysis

Comparative Analysis of Performance between Multimodal Implementation of Chatbot Based on News Classification Data Using Categories

Text Analysis and Visualization Research on the Hetu Dangse During the Qing Dynasty of China

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