In this paper, a method for knowledge expansion of metadata using script mining analysis for multimedia recommendation systems is proposed. The method allows the extraction of new metadata and knowledge expansion through the mining analysis of multimedia scripts, which include a large amount of information. The scripts are collected by a Web crawler based on Python. From the collected scripts, hidden information is extracted through keyword analysis and sentiment analysis. In keyword analysis, scripts, unlike general documents, show a high frequency of names of characters or proper nouns. Such names or proper nouns are not frequently used in other media content, and therefore, their importance is high. Frequently, they are already offered in the conventional metadata, and consequently cause information duplication. Accordingly, term frequency–inverse document and metadata frequency (TF–IDMF), which considers the frequency of metadata in general term frequency–inverse document frequency (TF–IDF), is used. Thus, the importance of the names of characters or proper nouns in scripts can be decreased. Because the keywords for the extracted scripts are in fact included in the scripts, they can be used for precise multimedia search and recommendation. In sentiment analysis, the AFINN lexicon and the Bing lexicon are utilized to scan words in a script. The Bing lexicon is used to examine whether the words in the entire script are positive or negative. Then, the total numbers of positive words and negative words are used to calculate the representative sentiment of the script. The AFINN lexicon includes approximately 170 sentiment words, the negative or positive sentiment of which is presented in the range − 5 to +5. One script is divided into 100 sentences, and then, the representative sentiment in each sentence is evaluated as either positive or negative. Through script scanning, the flow of sentiment in multimedia streams can be discovered. The Bing lexicon categorizes words into positive, negative, and neutral sentiments. Through script scanning, the words included in each category can be quantified. Depending on the result of the script sentiment analysis, a different sentence embedding method based on inter-sentence similarity is used to cluster similar media. The results of the keyword analysis and sentiment analysis of a script are added to the metadata in a new column in a knowledge base to expand knowledge. To evaluate the significance of multimedia recommendations, keywords and sentiment information are used, and then, the similarity and clustering of the extracted media are assessed. As a result, script mining analysis based on the attributes that include actual information of media is considerably better than that based on types or a range of metadata attributes. Therefore, the proposed knowledge expansion method achieves significant results and shows an excellent performance in multimedia recommendation.
