Hiromi Ishizaki

Feature Based Sentiment Analysis of Tweets in Multiple Languages

Feature based sentiment analysis is normally conducted using review Web sites, since it is difficult to extract accurate product features from tweets. However, Twitter users express sentiment towards a large variety of products in many different languages. Besides, sentiment expressed on Twitter is more up to date and represents the sentiment of a larger population than review articles. Therefore, we propose a method that identifies product features using review articles and then conduct sentiment analysis on tweets containing those features. In that way, we can increase the precision of feature extraction by up to 40% compared to features extracted directly from tweets. Moreover, our method translates and matches the features extracted for multiple languages and ranks them based on how frequently the features are mentioned in the tweets of each language. By doing this, we can highlight the features that are the most relevant for multilingual analysis.

Feature Analysis and Normalization Approach for Robust Content-Based Music Retrieval to Encoded Audio with Different Bit Rates

In order to achieve highly accurate content-based music information retrieval (MIR), it is necessary to compensate the various bit rates of encoded songs which are stored in the music collection, since the bit rate differences are expected to apply a negative effect to content-based MIR results. In this paper, we examine how the bit rate differences affect MIR results, propose methods to normalize MFCC features extracted from encoded files with various bit rates, and show their effects to stabilize MIR results.

Feature Space Modification for Content-Based Music Retrieval Based on User Preferences

This paper proposes a feature space modification method for feature extraction of music, which is effective for the development of a content-based music information retrieval (MIR) system based on user preferences. The proposed method conducts clustering of all songs in the music collection, and utilizes the resulting cluster IDs as training data for feature space modification, and is capable to automatically generate a feature space which is suitable to the content of any music collection. Experiment results prove that the proposed method improves accuracy of user preference based MIR

A coding system for dynamic topic analysis: a computer-mediated discourse analysis technique

We introduce an efficient coding system for dynamic topic analysis (DTA), a computer-mediated discourse analysis technique that codes and visualizes topical development over time in online discussions. Our system provides three main functionalities: intuitive coding with a touch screen interface, automated inter-rater agreement computation, and visualization of the coding results. Using the system, we conducted a preliminary DTA of 28,131 user comments from the popular music distribution sites SoundCloud and Last.fm. The analysis shows that most SoundCloud and Last.FM comments are narrowly on-topic and prompt-focused, as compared to discussions in other social media that exhibit more topical elaboration.

Auto-tagging of images in non-english languages using tag language conversion

Utilization of web images with social tags as training data has been a major trend for the development of automatic image tagging/classification systems. While the amount of information available on web sites such as Flickr is abundant, the majority of information obtained from such sites is in English, the dominant language on the web. This linguistic unbalance is expected to affect auto-tagging results in a negative way for non-English users, who demand image tags in their native languages. The objective of this research is to develop an image auto-tagging system which can generate tags in languages other than English. This paper examines the effect of linguistic unbalance in training data to construct auto-tagging systems, which aim to generate tags in minor languages. Furthermore, we propose methods which utilize an auto-tagging model generated on English training data, and convert the auto-tagging results to the target language. Subjective evaluations show that the proposed method is capable of generating auto-tagging results with better quality than conventional approaches.

Autocorrelation-based beat estimation adaptive to drastic tempo change in a song

Automatic beat estimation is an essential technology not only for fundamental analysis of music, but also for the development of advanced music applications such as DJ mixing. In this paper, we propose an autocorrelation based beat estimation method, which, unlike existing methods, is capable of accurately detecting the position of beats, even from songs with drastic change of tempo. Our proposal consists of two major steps. First, the approximate positions of the beats are pre-estimated by a long-sized analysis window. Next, the preestimated beats are verified by using a short analysis window. These steps are repetitively executed until the beat estimation results converge. Experiments conducted with audio signals and songs with artificially applied tempo changes have proved that our proposal can detect beats with high accuracy.