Data mining through sentiment analysis: Lexicon based sentiment analysis model using aspect catalogue

2016 ◽  
Author(s):  
Aman Mehto ◽  
Karnika Indras
Keyword(s):  
Data Mining ◽  
Sentiment Analysis ◽  
Analysis Model

Constructing a sentiment analysis model for LibQUAL+ comments

2017 ◽  
Vol 18 (1) ◽  
pp. 78-87 ◽  
Author(s):  
Michael Thomas Moore
Keyword(s):  
Data Mining ◽  
Sentiment Analysis ◽  
User Satisfaction ◽  
Three Dimensions ◽  
Information Control ◽  
Analysis Model ◽  
Content Type ◽  
Depth Analysis ◽  
Library As Place ◽  
Negative Sentiment

Purpose The purpose of this paper is to establish a data mining model for performing sentiment analysis on open-ended qualitative LibQUAL+ comments, providing a further method for year-to-year comparison of user satisfaction, both of the library as a whole and individual topics. Design/methodology/approach A training set of 514 comments, selected at random from five LibQUAL+ survey responses, was manually reviewed and labeled as having a positive or negative sentiment. Using the open-source RapidMiner data mining platform, those comments provided the framework for creating library-specific positive and negative word vectors to power the sentiment analysis model. A further process was created to help isolate individual topics within the larger comments, allowing for more nuanced sentiment analysis. Findings Applied to LibQUAL+ comments for a Canadian mid-sized academic research library, the model suggested a fairly even distribution of positive and negative sentiment in overall comments. When filtering comments into affect of service, information control and library as place, the three dimensions’ relative polarity mirrored the results of the quantitative LibQUAL+ questions, with highest scores for affect of service and lowest for library as place. Practical implications The sentiment analysis model provides a complementary tool to the LibQUAL+ quantitative results, allowing for simple, time-efficient, year-to-year analysis of open-ended comments. Furthermore, the process provides the means to isolate specific topics based on specified keywords, allowing individual institutions to tailor results for more in-depth analysis. Originality/value To best account for library-specific terminology and phrasing, the sentiment model was created using LibQUAL+ open-ended comments as the foundation for the sentiment model’s classification process. The process also allows individual topics, chosen to meet individual library needs, to be isolated and independently analyzed, providing more precise examination.

Sentiment analysis of students in ideological and political teaching based on artificial intelligence and data mining

2021 ◽  
pp. 1-10
Author(s):  
Wan Hongmei ◽  
Tang Songlin
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Sentiment Analysis ◽  
Machine Learning Algorithms ◽  
Classroom Teaching ◽  
Functional Modules ◽  
Semantic Ambiguity ◽  
Calculation Methods ◽  
Multiple Factors ◽  
Quantitative Calculation

In order to improve the efficiency of sentiment analysis of students in ideological and political classrooms, under the guidance of artificial intelligence ideas, this paper combines data mining and machine learning algorithms to improve and propose a method for quantifying the semantic ambiguity of sentiment words. Moreover, this paper designs different quantitative calculation methods of sentiment polarity intensity, and constructs video image sentiment recognition, text sentiment recognition, and speech sentiment recognition functional modules to obtain a combined sentiment recognition model. In addition, this article studies student emotions in ideological and political classrooms from the perspective of multimodal transfer learning, and optimizes the deep representation of images and texts and their corresponding deep networks through single-depth discriminative correlation analysis. Finally, this paper designs experiments to verify the model effect from two perspectives of single factor sentiment analysis and multi-factor sentiment analysis. The research results show that comprehensive analysis of multiple factors can effectively improve the effect of sentiment analysis of students in ideological and political classrooms, and enhance the effect of ideological and political classroom teaching.

An ANEW based Fuzzy Sentiment Analysis Model

2018 ◽  
Author(s):  
Andres Montoro ◽  
Jose A. Olivas ◽  
Arturo Peralta ◽  
Francisco P. Romero ◽  
Jesus Serrano-Guerrero
Keyword(s):  
Sentiment Analysis ◽  
Analysis Model

scholarly journals Sentiment analysis of MOOC reviews via ALBERT-BiLSTM model

2021 ◽  
Vol 336 ◽  
pp. 05008
Author(s):  
Cheng Wang ◽  
Sirui Huang ◽  
Ya Zhou
Keyword(s):  
Sentiment Analysis ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Analysis Model ◽  
Accuracy Rate ◽  
Massive Open Online ◽  
Data Set ◽  
Proposed Model ◽  
Contextual Feature

The accurate exploration of the sentiment information in comments for Massive Open Online Courses (MOOC) courses plays an important role in improving its curricular quality and promoting MOOC platform’s sustainable development. At present, most of the sentiment analyses of comments for MOOC courses are actually studies in the extensive sense, while relatively less attention is paid to such intensive issues as the polysemous word and the familiar word with an upgraded significance, which results in a low accuracy rate of the sentiment analysis model that is used to identify the genuine sentiment tendency of course comments. For this reason, this paper proposed an ALBERT-BiLSTM model for sentiment analysis of comments for MOOC courses. Firstly, ALBERT was used to dynamically generate word vectors. Secondly, the contextual feature vectors were obtained through BiLSTM pre-sequence and post-sequence, and the attention mechanism that could calculate the weight of different words in a sentence was applied together. Finally, the BiLSTM output vectors were input into Softmax for the classification of sentiments and prediction of the sentimental tendency. The experiment was performed based on the genuine data set of comments for MOOC courses. It was proved in the result that the proposed model was higher in accuracy rate than the already existing models.

scholarly journals PENGEMBANGAN INTELLIGENT DATA COLLECTOR UNTUK ANALISIS BIG DATA ARTIKEL BERITA ONLINE

2018 ◽  
Vol 3 (1) ◽  
pp. 49-59
Author(s):  
Zul Indra ◽  
Liza Trisnawati
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Text Summarization ◽  
Data Collector

Big data  telah menjadi salah satu topik yg paling menarik dalam dunia teknologi informasi sekarang ini. Salah satu sumber big data yang tersedia dan bebas diakses adalah artikel berita online. Dalam sehari, sebuah situs berita populer bisa menghasilkan lebih dari 100 artikel berita baru. Bayangkan berapa banyak jumlah halaman berita yang tersedia untuk kita baca sekarang ini. Sementara itu, tahap awal untuk melakukan analisis big data terhadap artikel berita online adalah data storing dan preprocessing. Berdasarkan pemikiran tersebut maka perlu dikembangkan suatu aplikasi yang bisa mengumpulkan artikel berita online secara otomatis untuk kemudian di analisis lebih lanjut. Penelitian ini bermaksud mengembangkan suatu aplikasi yang diberi nama dengan intelligent data collector (IDC) yang memudahkan kita untuk mengumpulkan artikel berita online. Aplikasi IDC ini mengumpulkan artikel berita online kemudian melakukan preprocessing terhadap artikel-artikel tersebut dan menyimpannya dalam database lokal. Database ini kemudian bisa digunakan lebih lanjut untuk berrbagai macam data mining proses seperti opinion mining (sentiment analysis), topic classification, text summarization dan lain sebagainya.

scholarly journals Conceptual Sentiment Analysis Model

2018 ◽  
Vol 8 (4) ◽  
pp. 2358 ◽  
Author(s):  
Kranti Vithal Ghag ◽  
Ketan Shah
Keyword(s):  
Sentiment Analysis ◽  
Relative Frequency ◽  
Syntactic Structure ◽  
Research Work ◽  
Average Frequency ◽  
Semantic Structure ◽  
Analysis Model ◽  
Inverse Document Frequency ◽  
Document Frequency ◽  
Improve Accuracy

<span>Bag-of-words approach is popularly used for Sentiment analysis. It maps the terms in the reviews to term-document vectors and thus disrupts the syntactic structure of sentences in the reviews. Association among the terms or the semantic structure of sentences is also not preserved. This research work focuses on classifying the sentiments by considering the syntactic and semantic structure of the sentences in the review. To improve accuracy, sentiment classifiers based on relative frequency, average frequency and term frequency inverse document frequency were proposed. To handle terms with apostrophe, preprocessing techniques were extended. To focus on opinionated contents, subjectivity extraction was performed at phrase level. Experiments were performed on Pang &amp; Lees, Kaggle’s and UCI’s dataset. Classifiers were also evaluated on the UCI’s Product and Restaurant dataset. Sentiment Classification accuracy improved from 67.9% for a comparable term weighing technique, DeltaTFIDF, up to 77.2% for proposed classifiers. Inception of the proposed concept based approach, subjectivity extraction and extensions to preprocessing techniques, improved the accuracy to 93.9%.</span>

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