Difference between revisions of "Towards fine grained extended targets in sentiment analysis"
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| + | == Comments == | ||
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| + | We're discussed this briefly. I like the basic idea, which I'm interpreting as taking existing sentiment targets, applying phrase-detection methods to extend them, and then looking for cases where the extended targets have different sentiment that the basic targets. | ||
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| + | It would be fun to see this on twitter, but hard to evaluate. You might look at testing the method quantitatively on a sentiment dataset that's been extensively labeled, like the [http://verbs.colorado.edu/jdpacorpus/ JD Powers dataset]. --[[User:Wcohen|Wcohen]] 15:46, 10 October 2012 (UTC) | ||
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== Team members == | == Team members == | ||
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== Introduction == | == Introduction == | ||
| − | A key motivation for doing sentiment analysis in social media is that many of the companies and individuals want to know what other people think about them. These target-dependent sentiment analysis tools has attracted much attention recently. Websites like | + | A key motivation for doing sentiment analysis in social media is that many of the companies and individuals want to know what other people think about them. These target-dependent sentiment analysis tools has attracted much attention recently. Websites like [http://www.tweetfeel.com/ Tweetfeel] and [http://www.sentiment140.com/ TwitterSentiment] has been set up. The task for these tools is very simple. Namely, when you put a company name or a person's name whatever you are interested, they will give you some tweets containing your input name and classify them into positive, negative (or neutral). |
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| + | For example: | ||
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| + | * @hilton_peggy it took me 2 hours to figure out [[Microsoft]] excel for my graph. I feel your pain. | ||
| + | * @_Jasmaniandevil i like watching [[Obama]] make a fool of himself ... | ||
| + | * Not going to [[CMU]] homecoming :( | ||
| + | |||
| + | You can see clearly from the examples that simply use a key-words-hit method in this task is not a very good way. Because these extended targets (Microsoft excel, Obama make a fool fo himself, Not going to CMU homecoming) is the real targets of the sentence, these extended targets themselves can make a difference in query name we input. Microsoft excel can be regarded as sentiment to Microsoft (with same polarity), Obama make a fool of himself can be regarded as sentiment to Obama (with opposite polarity) and CMU homecoming may have no sentiment transfered to CMU. Therefore, ways to use fine grained extended targets in this task is extremely important. | ||
== Task == | == Task == | ||
| − | + | We will trying to find patterns between extended targets and the input words (namely, orignal targets) on how we can extend a target, how these extensions affect the orignal targets in sentiment analysis tasks, and using these to improve the current state-of-art target-dependent sentiment classifier. | |
== Data == | == Data == | ||
| − | + | Want to use data presented in Long Jiang et al, Target-dependent Twitter Sentiment Classification, ACL 2011. Twitter Dataset and website like Tweetfeel and TwitterSentiment are useful resources. | |
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| − | |||
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| − | + | == Draft Plan/Method == | |
| − | + | * Find all the connections syntactically to see in which connection, context words can impact the orignal target. | |
| + | * Discuss patterns we can find in extended targets, for example, if it is a part-of relation or the orignal target is only a modifier etc. | ||
| + | * Try to find the group of words which have negative effect to the orignal target (things like "make fool of oneself") since that is an orignal of errors. | ||
== Related Work == | == Related Work == | ||
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| − | |||
| − | |||
| − | |||
| − | |||
| − | + | Long Jiang et al, Target-dependent Twitter Sentiment Classification, ACL 2011. | |
| − | + | Luciano Barbosa and Junlan Feng, Robust Sentiment Detection on Twitter from Biased and Noisy Data, Coling 2010 | |
Latest revision as of 11:46, 10 October 2012
Contents
Comments
We're discussed this briefly. I like the basic idea, which I'm interpreting as taking existing sentiment targets, applying phrase-detection methods to extend them, and then looking for cases where the extended targets have different sentiment that the basic targets.
It would be fun to see this on twitter, but hard to evaluate. You might look at testing the method quantitatively on a sentiment dataset that's been extensively labeled, like the JD Powers dataset. --Wcohen 15:46, 10 October 2012 (UTC)
Team members
Project Title
Towards fine grained extended targets in sentiment analysis
Introduction
A key motivation for doing sentiment analysis in social media is that many of the companies and individuals want to know what other people think about them. These target-dependent sentiment analysis tools has attracted much attention recently. Websites like Tweetfeel and TwitterSentiment has been set up. The task for these tools is very simple. Namely, when you put a company name or a person's name whatever you are interested, they will give you some tweets containing your input name and classify them into positive, negative (or neutral).
For example:
- @hilton_peggy it took me 2 hours to figure out Microsoft excel for my graph. I feel your pain.
- @_Jasmaniandevil i like watching Obama make a fool of himself ...
- Not going to CMU homecoming :(
You can see clearly from the examples that simply use a key-words-hit method in this task is not a very good way. Because these extended targets (Microsoft excel, Obama make a fool fo himself, Not going to CMU homecoming) is the real targets of the sentence, these extended targets themselves can make a difference in query name we input. Microsoft excel can be regarded as sentiment to Microsoft (with same polarity), Obama make a fool of himself can be regarded as sentiment to Obama (with opposite polarity) and CMU homecoming may have no sentiment transfered to CMU. Therefore, ways to use fine grained extended targets in this task is extremely important.
Task
We will trying to find patterns between extended targets and the input words (namely, orignal targets) on how we can extend a target, how these extensions affect the orignal targets in sentiment analysis tasks, and using these to improve the current state-of-art target-dependent sentiment classifier.
Data
Want to use data presented in Long Jiang et al, Target-dependent Twitter Sentiment Classification, ACL 2011. Twitter Dataset and website like Tweetfeel and TwitterSentiment are useful resources.
Draft Plan/Method
- Find all the connections syntactically to see in which connection, context words can impact the orignal target.
- Discuss patterns we can find in extended targets, for example, if it is a part-of relation or the orignal target is only a modifier etc.
- Try to find the group of words which have negative effect to the orignal target (things like "make fool of oneself") since that is an orignal of errors.
Related Work
Long Jiang et al, Target-dependent Twitter Sentiment Classification, ACL 2011.
Luciano Barbosa and Junlan Feng, Robust Sentiment Detection on Twitter from Biased and Noisy Data, Coling 2010
