Most previous work on multilingual sentiment analysis has focused on methods to adapt sentiment resources from resource-rich languages to resource—poor languages.

Another issue is to investigate how to improve multilingual sentiment analysis by exploiting comparable corpora.

The field of sentiment analysis has quickly attracted the attention of researchers and practitioners alike (e.g.

Indeed, sentiment analysis systems, which mine opinions from textual sources (e.g.

Previous work in multilingual sentiment analysis has therefore focused on methods to adapt sentiment resources (e.g.

Multilingual Sentiment Analysis .

There is a growing body of work on multilingual sentiment analysis .

Sentiment analysis on Twitter data has attracted much attention recently.

Previous work (Barbosa and Feng, 2010; Davidiv et al., 2010) has discovered many effective features for sentiment analysis of tweets, such as emoticons, punctuation, prior subjectivity and polarity of a word.

Twitter sentiment analysis has attracted much attention recently.

In fact, it is easy to find many such cases by looking at the output of Twitter Sentiment or other Twitter sentiment analysis web sites.

In addition, tweets are usually shorter and more ambiguous than other sentiment data commonly used for sentiment analysis , such as reviews and blogs.

In recent years, sentiment analysis (SA) has become a hot topic in the NLP research community.

As Twitter becomes more popular, sentiment analysis on Twitter data becomes more attractive.

If SPM were yoked with sentiment analysis , we might identify which opinions belong to respected members of online communities or lay the groundwork for understanding how respect is earned in social networks.

Closely related natural language processing problems are authorship attribution, sentiment analysis , emotion detection, and personality classification: all aim to extract higher-level information from language.

Sentiment analysis , which strives to determine the attitude of an author from text, has recently garnered much attention (e.g.

Though there are many annotated data sets for the research of speech summarization and sentiment analysis , there is no corpus available for opinion summarization on spontaneous speech.

Both sentiment analysis (opinion recognition) and summarization have been well studied in recent years in the natural language processing (NLP) community.

Most of the previous work on sentiment analysis has been conducted on reviews.

However, this problem is challenging in that: (a) Summarization in spontaneous speech is more difficult than well structured text (Mckeown et al., 2005), because speech is always less organized and has recognition errors when using speech recognition output; (b) Sentiment analysis in dialogues is also much harder because of the genre difference compared to other domains like product reviews or news resources, as reported in (Raaijmakers et al., 2008); (c) In conversational speech, information density is low and there are often off topic discussions, therefore presenting a need to identify utterances that are relevant to the topic.