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Sentiment Analysis of Social Networking Data Using Categorized Dictionary | ||
| Journal of Information Technology Management | ||
| دوره 12، شماره 4، 2020، صفحه 105-120 اصل مقاله (1.16 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2020.297562.2465 | ||
| نویسندگان | ||
| Akansha Singh* 1؛ Aastha Sharma2؛ Krishna Kant Singh3؛ Anuradha Dhull4 | ||
| 1Associate Prof., Department of CSE, ASET, Amity University Uttar Pradesh, Noida. | ||
| 2The North Cap University, Gurgaon, India. | ||
| 3Associate Prof., Department of ECE, KIET Group of Institutions, Ghaziabad, India. | ||
| 4Assistant Prof., The North Cap University, Gurgaon, India. | ||
| چکیده | ||
| Sentiment analysis is the process of analyzing a person’s perception or belief about a particular subject matter. However, finding correct opinion or interest from multi-facet sentiment data is a tedious task. In this paper, a method to improve the sentiment accuracy by utilizing the concept of categorized dictionary for sentiment classification and analysis is proposed. A categorized dictionary is developed for the sentiment classification and further calculation of sentiment accuracy. The concept of categorized dictionary involves the creation of dictionaries for different categories making the comparisons specific. The categorized dictionary includes words defining the positive and negative sentiments related to the particular category. It is used by the mapper reducer algorithm for the classification of sentiments. The data is collected from social networking site and is pre-processed. Since the amount of data is enormous therefore a reliable open-source framework Hadoop is used for the implementation. Hadoop hosts various software utilities to inspect and process any type of big data. The comparative analysis presented in this paper proves the worthiness of the proposed method. | ||
| کلیدواژه‌ها | ||
| Hadoop؛ Big data؛ HDFS؛ Map-Reduce؛ Facepager؛ Sentiment Analysis | ||
| مراجع | ||
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