Aspektbaserad Sentimentanalys för Business Intelligence inom E-handeln

Detta är en Kandidat-uppsats från KTH/Skolan för elektroteknik och datavetenskap (EECS)

Sammanfattning: Many companies strive to make data-driven decisions. To achieve this, they need to explore new tools for Business Intelligence. The aim of this study was to examine the performance and usability of aspect-based sentiment analysis as a tool for Business Intelligence in E-commerce. The study was conducted in collaboration with Ellos Group AB which supplied anonymous customer feedback data. The implementation consists of two parts, aspect extraction and sentiment classification. The f irst part, aspect extraction, was implemented using dependency parsing and various aspect grouping techniques. The second part, sentiment classification, was implemented using the language model KB-BERT, a Swedish version of the BERT model. The method for aspect extraction achieved a satisfactory precision of 79,5% but only a recall of 27,2%. Moreover, the result for sentiment classification was unsatisfactory with an accuracy of 68,2%. Although the results underperform expectations, we conclude that aspect-based sentiment analysis in general is a great tool for Business Intelligence. Both as a means of generating customer insights from previously unused data and to increase productivity. However, it should only be used as a supportive tool and not to replace existing processes for decision-making.

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