Intelligent Performance Enhancement of Sentiment Analysis for Social Data Collection in Big Data Management

With the widespread popularity of social networks, effective data analysis is the current research focus. This study focuses on improving the performance of sentiment analysis in social data collection under the big data management framework. Social network datasets have become an important source of understanding social behavior and providing effective information. With the widespread application of the magic of big data, the scale of these highly concurrent, rigidly complex data sets continues to increase, which requires powerful methods to analyze the data while addressing user preference issues. Therefore, this study proposes a new intelligent approach model to enhance sentiment analysis by enhancing the big data management tools and techniques. Key methodologies include using advanced data processing frameworks such as R and Python for text mining and social network analysis, leveraging Spark's distributed processing capabilities, and implementing stable indexing and query capabilities through Elasticsearch. In addition, the research integrates efficient web crawling technology and secure data access protocols (such as OAuth 2.0) for social data collection. Experimental results conducted on Tencent Weibo data show that the accuracy of sentiment analysis significantly improves over multiple tests, highlighting the effectiveness of the proposed methodology. The accuracy gradually improved from the initial 78.5% to 93.5%, confirming the reliability of the implemented algorithm and framework.