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Phonetic-enriched Text Representation for Chinese Sentiment Analysis with Reinforcement Learning
The Chinese pronunciation system offers two characteristics that disting...
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Radical-level Ideograph Encoder for RNN-based Sentiment Analysis of Chinese and Japanese
The character vocabulary can be very large in non-alphabetic languages s...
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RETUYT in TASS 2017: Sentiment Analysis for Spanish Tweets using SVM and CNN
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Building domain specific lexicon based on TikTok comment dataset
In the sentiment analysis task, predicting the sentiment tendency of a s...
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BowTie - A deep learning feedforward neural network for sentiment analysis
How to model and encode the semantics of human-written text and select t...
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UMDSub at SemEval-2018 Task 2: Multilingual Emoji Prediction Multi-channel Convolutional Neural Network on Subword Embedding
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Sentiment analysis in Bengali via transfer learning using multi-lingual BERT
Sentiment analysis (SA) in Bengali is challenging due to this Indo-Aryan...
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Exploiting Effective Representations for Chinese Sentiment Analysis Using a Multi-Channel Convolutional Neural Network
Effective representation of a text is critical for various natural language processing tasks. For the particular task of Chinese sentiment analysis, it is important to understand and choose an effective representation of a text from different forms of Chinese representations such as word, character and pinyin. This paper presents a systematic study of the effect of these representations for Chinese sentiment analysis by proposing a multi-channel convolutional neural network (MCCNN), where each channel corresponds to a representation. Experimental results show that: (1) Word wins on the dataset of low OOV rate while character wins otherwise; (2) Using these representations in combination generally improves the performance; (3) The representations based on MCCNN outperform conventional ngram features using SVM; (4) The proposed MCCNN model achieves the competitive performance against the state-of-the-art model fastText for Chinese sentiment analysis.
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