A survey on sentiment analysis methods, applications, and challenges

被引:345
作者
Wankhade, Mayur [1 ,2 ]
Rao, Annavarapu Chandra Sekhara [1 ,2 ]
Kulkarni, Chaitanya [1 ,2 ]
机构
[1] Indian Inst Technol ISM, Dept Comp Sci & Engn, Dhanbad 826004, Bihar, India
[2] Dayananda Sagar Coll Engn, Bangalore 560078, Karnataka, India
基金
英国科研创新办公室;
关键词
Sentiment analysis; Text analysis; Word embedding; Machine learning; Social media; CONVOLUTIONAL NEURAL-NETWORK; SUPPORT VECTOR MACHINE; FEATURE-SELECTION; ASPECT EXTRACTION; HYBRID APPROACH; SOCIAL MEDIA; CLASSIFICATION; TEXT; ALGORITHMS; FRAMEWORK;
D O I
10.1007/s10462-022-10144-1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The rapid growth of Internet-based applications, such as social media platforms and blogs, has resulted in comments and reviews concerning day-to-day activities. Sentiment analysis is the process of gathering and analyzing people's opinions, thoughts, and impressions regarding various topics, products, subjects, and services. People's opinions can be beneficial to corporations, governments, and individuals for collecting information and making decisions based on opinion. However, the sentiment analysis and evaluation procedure face numerous challenges. These challenges create impediments to accurately interpreting sentiments and determining the appropriate sentiment polarity. Sentiment analysis identifies and extracts subjective information from the text using natural language processing and text mining. This article discusses a complete overview of the method for completing this task as well as the applications of sentiment analysis. Then, it evaluates, compares, and investigates the approaches used to gain a comprehensive understanding of their advantages and disadvantages. Finally, the challenges of sentiment analysis are examined in order to define future directions.
引用
收藏
页码:5731 / 5780
页数:50
相关论文
共 246 条
  • [1] Spam Email Detection Using Deep Learning Techniques
    AbdulNabi, Isra'a
    Yaseen, Qussai
    [J]. 12TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT) / THE 4TH INTERNATIONAL CONFERENCE ON EMERGING DATA AND INDUSTRY 4.0 (EDI40) / AFFILIATED WORKSHOPS, 2021, 184 : 853 - 858
  • [2] Sentiment analysis through recurrent variants latterly on convolutional neural network of Twitter
    Abid, Fazeel
    Alam, Muhammad
    Yasir, Muhammad
    Li, Chen
    [J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2019, 95 : 292 - 308
  • [3] Transformer models for text-based emotion detection: a review of BERT-based approaches
    Acheampong, Francisca Adoma
    Nunoo-Mensah, Henry
    Chen, Wenyu
    [J]. ARTIFICIAL INTELLIGENCE REVIEW, 2021, 54 (08) : 5789 - 5829
  • [4] Text-based emotion detection: Advances, challenges, and opportunities
    Acheampong, Francisca Adoma
    Chen Wenyu
    Nunoo-Mensah, Henry
    [J]. ENGINEERING REPORTS, 2020, 2 (07)
  • [5] Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
    Adomavicius, Gediminas
    Kwon, YoungOk
    [J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2012, 24 (05) : 896 - 911
  • [6] Detection and classification of social media-based extremist affiliations using sentiment analysis techniques
    Ahmad, Shakeel
    Asghar, Muhammad Zubair
    Alotaibi, Fahad M.
    Awan, Irfanullah
    [J]. HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES, 2019, 9
  • [7] A review of feature selection techniques in sentiment analysis
    Ahmad, Siti Rohaidah
    Abu Bakar, Azuraliza
    Yaakub, Mohd Ridzwan
    [J]. INTELLIGENT DATA ANALYSIS, 2019, 23 (01) : 159 - 189
  • [8] How Intense Are You? Predicting Intensities of Emotions and Sentiments using Stacked Ensemble
    Akhtar, Md Shad
    Ekbal, Asif
    Cambria, Erik
    [J]. IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE, 2020, 15 (01) : 64 - 75
  • [9] Aspect based Sentiment Oriented Summarization of Hotel Reviews
    Akhtar, Nadeem
    Zubair, Nashez
    Kumar, Abhishek
    Ahmad, Tameem
    [J]. 7TH INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING & COMMUNICATIONS (ICACC-2017), 2017, 115 : 563 - 571
  • [10] Random Forest and Support Vector Machine based Hybrid Approach to Sentiment Analysis
    Al Amrani, Yassine
    Lazaar, Mohamed
    El Kadiri, Kamal Eddine
    [J]. PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING IN DATA SCIENCES (ICDS2017), 2018, 127 : 511 - 520