I need a reliable and accurate method to filter tweets as subjective or objective. In other words I need to build a filter in something like Weka using a training set.
Are there any training sets available which could be used as a subjective/objective classifier for Twitter messages or other domains which may be transferable?
For research and non-profit purposes, SentiWordNet gives you exactly what you want. A commercial license is available too.
SentiWordNet : http://sentiwordnet.isti.cnr.it/
Sample Jave Code: http://sentiwordnet.isti.cnr.it/code/SWN3.java
Related Paper: http://nmis.isti.cnr.it/sebastiani/Publications/LREC10.pdf
The other approach I would try:
Example
Tweet 1: #xyz u should see the dark knight. Its awesme.
1) First a dictionary lookup for the for meanings.
"u" and "awesme" will not return anything.
2) Then go against the known abbreviations/shorthands and substitute matches with the expansions
(Some resources: netlingo http://www.netlingo.com/acronyms.php or smsdictionary http://www.smsdictionary.co.uk/abbreviations)
Now the original tweet will look like:
Tweet 1: #xyz you should see the dark knight. Its awesme.
3) Then feed the remaining words in the spell checker and substitute with the best match (not always ideal and error prone for small words)
Related Link:
Looking for Java spell checker library
Now the original tweet will look like:
Tweet 1: #xyz you should see the dark knight. Its awesome.
4) Split and feed the tweet into SWN3, aggregate the result
The problem with this approach is that
a) Negations should be handled outside SWN3.
b) Information in emoticons and exaggerated punctuations will be lost or they need to be handled separately.
There is sentiment training data at CMU somewhere. I can't remember the link. CMU has done a lot on twitter and sentiment analysis:
From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series
Carnegie Mellon Study of Twitter Sentiments Yields Results Similar to Public Opinion Polls
I wrote an english vs. not english Naive Bayes classifier for twitter and made a ~example dev/test set and it was 98% accurate. I think that sort of thing is always pretty good if you are just trying to understand the problem, but a package like SentiWordNet might give you a head start.
The problem is defining what makes a tweet subjective or objective! It's important to understand that machine learning is less about the algorithm and more about the quality of the data.
You mention 75% accuracy is all you need.... what about recall? If you provide the right training data you might be able to get that, at the cost of lower recall.
The DynamicLMClassifier in LingPipe works pretty good.
http://alias-i.com/lingpipe/demos/tutorial/sentiment/read-me.html
Related
I would like to be able to curate definitional summaries for a specific term from a textbook.
For example, from a Biology textbook, I would like to be able form a concise summary for the word "mitochondria". I have tried this by first parsing through the textbook for all sentences that contain the word "mitochondria", and feeding those sentences through summarization algorithms such as TextRank and LexRank, but those algorithms were not able to determine "definitional" sentences that well.
By definitional summaries, I mean useful sentences as far as a definition goes. For example, the sentence "The mitochondria is the powerhouse of the cell" would be a definitional sentence while the sentence "Fungal cells also contain mitochondria and a complex system of internal membranes, including the endoplasmic reticulum and Golgi apparatus" is not really pertinent to the definition of the mitochondria.
Any help or leads would be very much appreciated
There isn't really a straightforward way to do this, but you do have some options:
Just use a regex for "mitochondria is". It is the stupidest possible thing, but given a textbook it might prove satisfactory. It's simple enough testing should be easy, and at worst provides a baseline to compare alternatives to.
Run a parser (example: Stanford Parser) on each sentence with the word "mitochondria", and extract sentences where mitochondria is the subject. This would eliminate the negative example you gave. You would have to tune this, perhaps restricting main verbs, accounting for coordinators, and so on.
Use Information Extraction (example: Stanford OpenIE) to get a list of facts about mitochondria (like is-in(mitochondria, cell)) and do something with that.
This is a very open ended question. I can try to point how I would approach this...
One way would be to use some kind of vector representation for text (word2vec
or sent2vec come to mind).
Then by encoding the average of the sentences in vector format and checking the cosine similarity of this and of the term you seek, you could be getting something close to the definitional sentences you seek.
Even testing the cosine similarity of the averaged sentences you get out of the summary algorithm and the term might get you close to judge how close you are
I recently installed PredictionIO.
What I'd like to achieve is: I'd like to categorize content on the words included in the text. But how can I import data like raw Tweets to PredictionIO? Is it possible to let PredictionIO run over the content and find strong words and to sort them in categories?
What I'd like to get is something like this: Query for Boston Red Sox --> keywords that should appear would be: baseball, Boston, sports, ...
So I'll add on a little to what Thomas said. He's right, it all depends whether or not you have labels associated to your tweets. If your data is labeled then this will be a Text Classification problem. Look at this for more detailed info:
If you're instead looking to cluster, or group, a set of unlabeled observations then, as Thomas said, your best bet is to incorporate LDA into the works. Look at the latter documentation for more information, but basically once you run the LDA model you'll obtain an object of type DistributedLDAModel which has a method topicDistributions which gives you, for each tweet, a vector where each component is associated to a topic, and the component entry gives you the probability that the tweet belongs to that topic. You can cluster by assigning each tweet the topic with highest probability.
You also have access to a matrix of size MxN, where M is the number of words in your vocabulary, and N is the number of topics, or clusters, you wish to discover in your data. You can roughly interpret the ij th entry of this Topics Matrix as the probability that the word i appears in a document given that the document belongs to topic j. Another rule you could use for clustering is to treat each word vector associated to your tweets as a vector of counts. Then, you can interpret the ij entry of the product of your word matrix (tweets as rows, words as columns) and the Topics Matrix returned by LDA as the probability that tweet i belongs to topic j (this follows under certain assumptions, feel free to ask if you want more details). Again now you assign tweet i to the topic associated to the largest numerical value in row i of the resulting matrix. You can even use this clustering rule for assigning topics to incoming observations once you have used your original set of tweets for topic discovery!
Now, for data processing, you can still use the Text Classification reference for transforming your Tweets to word count vectors via the DataSource and Preparator components. As for importing your data, if you have the tweets saved locally on a file, you can use PredictionIO's Python SDK to import your data. An example is also given in the classification reference.
Feel free to ask any questions if anything isn't clear, and good luck!
So, really depends on if you have labelled data.
For example:
Baseball :: "I love Boston Red Sox #GoRedSox"
Sports :: "Woohoo! I love sports #winning"
Boston :: "Baseball time at Fenway Park. Red Sox FTW!"
...
Then you would be able to train a model to classifying Tweets against these keywords. You might be interested in templates for MLlib Naive Bayes, Decision Trees.
If you don't have labelled data (really, who wants to manually label Tweets) you might be able to use approaches such as Topic Modeling (e.g., LDA).
I don't think there is a template for LDA but being an active open source project it wouldn't surprise me if someone has already implemented this so might be a good idea to ask on PredictionIO user or dev forums.
I need a list of positive and negative words with the weights assigned to words according to how strong and week they are. I have got :
1.) WordNet - It gives a + or - score for every word.
2.) SentiWordNet - Giving positive and negative values in the range [0,1].
I checked these on few words,
love - wordNet is giving 0.0 for both noun and verb, I dont know why i think it should be positive by at least some factor.
repress - wordNet gives -9.93
- SentiWordNet gives - 0.0 for both pos and neg. (should be negative)
repose - wordNet - 2.488
- SentiWordNet - { pos - 0.125, neg - 0.5 } (should be positive)
I need some help to decide which one to use.
Thanks.
Quite often the degree and/or polarity may depend on the domain and/or the context, so the word alone isn't really enough to make a decision.
If you have some annotated data, I suggest training a classifier on that using the scores provided by the two resources as features. If you don't, one option is to use one of the available sentiment-annotated corpora that matches the domain in question. Without any data at all the whole task becomes somewhat tricky, although there is a substantial body of work on unsupervised approaches to sentiment classification, I believe, see, e.g. Unsupervised Sentiment Analysis
There is an interface to give different opinions for SentiWordNet, if you think they are wrong:
http://sentiwordnet.isti.cnr.it/search.php?q=repose
I downloaded latest Wordnet 3.1, and checked the file format documentation, and don't see any mention of the sentiment numbers you mention. It is also not shown in the online search.
So, for both those reasons I'd suggest going with SentiWordNet!
(I see your question is a year old, so perhaps you can tell us what you did go with, and why?)
The degree of the polarity depends not only on the words alone but also on the context of the sentece or the phrase.
SO if there are different results regarding the same word then it is because of the difference in the context.
I'm doing a small research project where I should try to split financial news articles headers to positive and negative classes.For classification I'm using SVM approach.The main problem which I see now it that not a lot of features can be produced for ML. News articles contains a lot of Named Entities and other "garbage" elements (from my point of view of course).
Could you please suggest ML features which can be used for ML training? Current results are: precision =0.6, recall=0.8
Thanks
The task is not trivial at all.
The straightforward approach would be to find or create a training set. That is a set of headers with positive news and a set of headers with negative news.
You turn the training set to a TF/IDF representation and then you train a Linear SVM to separate the two classes. Depending on the quality and size of your training set you can achieve something decent - not sure for 0.7 break even point.
Then, to get better results you need to go for NLP approaches. Try use a part-of-speech tagger to identify adjectives (trivial), and then score them using some sentiment DB like SentiWordNet.
There is an excellent overview on Sentiment Analysis by Bo Pang and Lillian Lee you should read:
How about these features?
Length of article header in words
Average word length
Number of words in a dictionary of "bad" words, e.g. dictionary = {terrible, horrible, downturn, bankruptcy, ...}. You may have to generate this dictionary yourself.
Ratio of words in that dictionary to total words in sentence
Similar to 3, but number of words in a "good" dictionary of words, e.g. dictionary = {boon, booming, employment, ...}
Similar to 5, but use the "good"-word dictionary
Time of the article's publication
Date of the article's publication
The medium through which it was published (you'll have to do some subjective classification)
A count of certain punctuation marks, such as the exclamation point
If you're allowed access to the actual article, you could use surface features from the actual article, such as its total length and perhaps even the number of responses or the level of opposition to that article. You could also look at many other dictionaries online such as Ogden's 850 basic english dictionary, and see if bad/good articles would be likely to extract many words from those. I agree that it seems difficult to come up with a long list (e.g. 100 features) of useful features for this purpose.
iliasfl is right, this is not a straightforward task.
I would use a bag of words approach but use a POS tagger first to tag each word in the headline. Then you could remove all of the named entities - which as you rightly point out don't affect the sentiment. Other words should appear frequently enough (if your dataset is big enough) to cancel themselves out from being polarised as either positive or negative.
One step further along, if you still aren't close could be to only select the adjectives and verbs from the tagged data as they are the words that tend to convey the emotion or mood.
I wouldn't be too disheartened in your precision and recall figures though, an F number of 0.8 and above is actually quite good.
Can you show me a simple example using http://www.nltk.org/code to determine if a string about a happy or upset mood?
NLTK cannot out of the box, but if you are looking for some related research on that area, take a look at this paper on Offensive Language Detection. The same methods could be adapted to detect comments which are not offensive/unoffensive, but instead happy/unhappy. The primary software package being used in this project for text classification is called WEKA and uses multiple classifiers, trained on previous examples, to determine whether language is offensive or not (and in this method uses a tunable threshold).
Pattern is something worthwhile a test drive too: you can see two opinion mining experiments right on the project homepage.
http://www.clips.ua.ac.be/pages/pattern-examples-100days
http://www.clips.ua.ac.be/pages/pattern-examples-elections
Nopey.
This is a task far beyond the capabilities of NLTK or any grammatical parser that is known or can be realistically imagined. Look at the NLTK Book to see what sorts of tasks it can accomplish which are far, far from your stated purpose.
As a cheap example:
I really enjoyed using your paper to train my dog.
Parse that up with NLTK and you can get
[('I', 'PRP'), ('really', 'RB'), ('enjoyed', 'VBD'),
('using', 'VBG'), ('your', 'PRP$'), ('paper', 'NN'),
('to', 'TO'), ('train', 'VB'), ('my', 'PRP$'), ('dog', 'NN')]
Where the parse tree would tell me that 'enjoyed' is the central (past-tense) verb of the simple sentence. To enjoy something is good. To train something is generally a good thing. Gerunds, nouns, comparatives, and such are relatively neutral. So give this a Good score of 0.90.
Except I really mean that I either hit my dog with your paper or let it excrete on the paper which you'd probably consider a not Good thing.
Hire a person for this recognition task.
Added for those who imagine that even trained classifiers are of much use:
Classify this real entry from a real customer review corpus using any classifier you like trained on any dataset you like:
This camera keeps on autofocussing in
auto mode with a buzzing sound which
can't be stopped. It would be really
good if they have given an option to
stop this autofocussing. If you want
to have the date and time on the
image, it's only through their
software which reads the image's date
and time from the image's meta-data.
So if you use your card reader and
copy images - you got to once again
open them through their software to
put the date and time. In that too,
there isn't a direct way to add date
and time
- you got to say 'print images' to a different directory in which there is
an option to specify the date and time
. Even the slightest of the shakes
totally distorts your image. Indoor
images weren't so clear. You got to
have flash 'on' to get it even though
your room is well lit. The lens cap is
a really annoying. the movie clips
taken will always have some 'noise' in
it - you can't avoid that.
The worst mood classification I obtained was "totally equivocal" yet humans can easily determine that this is anything but complimentary. This wasn't a randomly picked datum, rather one that was selected for negative bias without "hate" or "suxz" or similar.
You're looking for a technique that uses a machine learning classifier to determine whether a piece of text is positive or negative. There have been various different attempts at this by a number of research teams (e.g. http://research.yahoo.com/pub/2387 and http://lingcog.iit.edu/doc/appraisal_sentiment_cikm.pdf) we can get about 80% to 90% accuracy at determining whether a product review is positive or negative.
Due to the brevity of your question, it's not obvious to me whether determining whether a product review is positive or negative is the same task you're trying to accomplish, or merely a related task, but I'd suggest starting simple with bag-of-words classification with a Bayesian classifier (which NLTK should be able to handle), and then improve your techniques from there depending on how the accuracy turns out.
Unfortunately, I've never used NLTK (nor Python for that matter) so I can't give you a code example of how to use NLTK for this.