Christine Dewi, Gouwei Dai, Henoch Juli Christanto
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引用次数: 0
摘要
情感分析(SA)是指利用自然语言处理(NLP)方法来识别给定文本所传达的情感。本研究基于来自互联网电影数据库(IMDB)的数据集,其中包含对电影的评价及其相应的正面或负面分类。我们的研究实验旨在确定具有最高准确性和通用性的模型。我们的研究采用了多种分类器,包括无监督学习方法,如 Valence Aware Dictionary and sEntiment Reasoner (VADER) 和 Text Blob,以及监督学习方法,如 Naïve Bayes,其中包括 Bernoulli NB 和 Multinomial NB。我们还使用了几种方法,包括计数矢量器和词频-反向文档频率模型(TFIDF)矢量器。随后,利用长短期记忆(LSTM)基础模型等各种嵌入方法,执行词嵌入和双向 LSTM。最后,GloVe 嵌入以 90.64% 的准确率和 91.07% 的灵敏度实现了最佳性能。
Analysis of Internet Movie Database with Global Vectors for Word Representation
Sentiment analysis (SA) involves utilizing natural language processing (NLP) methods to identify the sentiment conveyed by a given text. This study is grounded on the dataset sourced from the internet movie database (IMDB), encompassing evaluations of films and their corresponding positive or negative classifications. Our research experiment aims to ascertain the model with the highest accuracy and generality. Our research utilizes diverse classifiers, comprising unsupervised learning approaches such as Valence Aware Dictionary and sEntiment Reasoner (VADER) and Text Blob, alongside Supervised Learning methods like Naïve Bayes, which encompasses both the Bernoulli NB and Multinomial NB. Several methodologies have been utilized, including the Count Vectorizer, and the Term Frequency-Inverse Document Frequency model (TFIDF) Vectorizer. Subsequently, word embedding and bidirectional LSTM are executed, utilizing various embeddings such as the Long Short-Term Memory (LSTM) base model. Finally, GloVe embeddings achieve the best performance with an accuracy of 90.64% and a sensitivity of 91.07%.