Emotion Expression Extraction Method for Chinese Microblog Sentences

With the rapid spread of Chinese microblog, a large number of microblog topics are being generated in real-time. More and more users pay attention to emotion expressions of these opinionated sentences in different topics. It is challenging to label the emotion expressions of opinionated sentences manually. For this endeavor, an emotion expression extraction method is proposed to process millions of user-generated opinionated sentences automatically in this paper. Specifically, the proposed method mainly contains two tasks: emotion classification and opinion target extraction. We first use a lexicon-based emotion classification method to compute different emotion values in emotion label vectors of opinionated sentences. Then emotion label vectors of opinionated sentences are revised by an unsupervised emotion label propagation algorithm. After extracting candidate opinion targets of opinionated sentences, the opinion target extraction task is performed on a random walk-based ranking algorithm, which considers the connection between candidate opinion targets and the textual similarity between opinionated sentences, ranks candidate opinion targets of opinionated sentences. Experimental results demonstrate the effectiveness of algorithms in the proposed method

Role of sentiment classification in sentiment analysis: a survey

Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results

Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Mengunakan Algoritma Support Vector Machine (SVM)

Analisis Sentimen merupakan cabang dari penelitian text mining yang melakukan proses pengklasifikasian dokumen teks. Analisis sentimen dapat melakukan ekstraksi pendapat, emosi, dan evaluasi tertulis seseorang tentang topik tertentu menggunakan teknik pemrosesan Bahasa alami. Pada penelitian ini melakukan analisis sentiment terhadap penggunaan aplikasi Shopee menggunakan algoritma Support Vector Machine (SVM). Tujuan dari penelitian ini adalah untuk mengklasifikasi data komentar dari pengguna aplikasi Shopee kedalam komentar positif dan negatif dengan mempelajari pendapat pengguna tentang aplikasi Shopee melalui ulasan yang diberikan, dan untuk mengetahui kinerja dari metode pengklasifikasi yang digunakan. Pada penelitian ini data diperoleh dengan cara mengangkat data dari ulasan penggunakan aplikasi Shopee menggunakan metode scraping dan berhasil mendapat 3000 data ulasan. Hasil penelitian menggunakan algoritma Support Vector Machine terbukti mampu menghasilkan kinerja yang cukup baik dengan hasil akurasi sebesar 98% dan f1-score sebesar 0.98 atau sebesar 98%.Sentiment analysis is a branch of text mining research that carries out the process of classifying text documents. Sentiment analysis can extract one's opinions, emotions, and evaluations about a certain topic using natural language techniques. In this study, sentiment analysis was carried out on the use of the Shopee application using the Support Vector Machine (SVM) algorithm. The purpose of this study is to classify comment data from Shopee application users, positive and negative comments by studying user opinions about the Shopee application through the reviews provided, and to determine the performance of the classifier method used. In this study, the data was obtained by collecting data from reviews on the use of the Shopee application using the scraping method and managed to get 3000 data reviews. The results of research using the Support Vector Machine algorithm are proven to be able to produce quite good performance with an accuracy of 98% and an f1-score of 0.98 or 98%.

Opinion Mining Summarization and Automation Process: A Survey

In this modern age, the internet is a powerful source of information. Roughly, one-third of the world population spends a significant amount of their time and money on surfing the internet. In every field of life, people are gaining vast information from it such as learning, amusement, communication, shopping, etc. For this purpose, users tend to exploit websites and provide their remarks or views on any product, service, event, etc. based on their experience that might be useful for other users. In this manner, a huge amount of feedback in the form of textual data is composed of those webs, and this data can be explored, evaluated and controlled for the decision-making process. Opinion Mining (OM) is a type of Natural Language Processing (NLP) and extraction of the theme or idea from the user's opinions in the form of positive, negative and neutral comments. Therefore, researchers try to present information in the form of a summary that would be useful for different users. Hence, the research community has generated automatic summaries from the 1950s until now, and these automation processes are divided into two categories, which is abstractive and extractive methods. This paper presents an overview of the useful methods in OM and explains the idea about OM regarding summarization and its automation process

Sentiment analysis for e-commerce product reviews in Chinese based on sentiment lexicon and deep learning

In recent years, with the rapid development of Internet technology, online shopping has become a mainstream way for users to purchase and consume. Sentiment analysis of a large number of user reviews on e-commerce platforms can effectively improve user satisfaction. This paper proposes a new sentiment analysis model-SLCABG, which is based on the sentiment lexicon and combines Convolutional Neural Network (CNN) and attention-based Bidirectional Gated Recurrent Unit (BiGRU). In terms of methods, the SLCABG model combines the advantages of sentiment lexicon and deep learning technology, and overcomes the shortcomings of existing sentiment analysis model of product reviews. The SLCABG model combines the advantages of the sentiment lexicon and deep learning techniques. First, the sentiment lexicon is used to enhance the sentiment features in the reviews. Then the CNN and the Gated Recurrent Unit (GRU) network are used to extract the main sentiment features and context features in the reviews and use the attention mechanism to weight. And finally classify the weighted sentiment features. In terms of data, this paper crawls and cleans the real book evaluation of dangdang.com, a famous Chinese e-commerce website, for training and testing, all of which are based on Chinese. The scale of the data has reached 100000 orders of magnitude, which can be widely used in the field of Chinese sentiment analysis. The experimental results show that the model can effectively improve the performance of text sentiment analysis