I am interested in looking at the entity or aspect level sentiment for various tweets. Are there any models that have already looked at this problem? I tried looking, but I couldn't really find anything. If not, how would I go about creating a custom model?
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Hi I am trying to analyse descriptions of around 30000 requests to identify common requests as the data has no tags or titles.
I’ve looked at a lot of content on sentiment analysis and I’m currently thinking I need to train a model from a small random sample to better classify the data.
Is there a better approach I should be following?
Before answering your question, I would say what you're looking for has similar solutions to sentiment analysis but is a different case.
If you want to group any documents you have 2 methods to move on with in AI.
1- Supervised Learning (Classifying)
2- Unsupervised Learning (Clustering)
In your case as there is no labeled (tagged) data, then clustering is more convenient.
You can generate the tf-idf vector and use it as the feature for each word and document in descriptions and cluster the data based on that.
Depending on the coding language you use there are a lot of examples on the web but for java you can check out below links,
TextAnalyzer
Carrot Clustering
I have a project where I need to analyze a text to extract some information if the user who post this text need help in something or not, I tried to use sentiment analysis but it didn't work as expected, my idea was to get the negative post and extract the main words in the post and suggest to him some articles about that subject, if there is another way that can help me please post it below and thanks.
for the dataset i useed, it was a dataset for sentiment analyze, but now I found that it's not working and I need a dataset use for this subject.
Please use the NLP methods before processing the sentiment analysis. Use the TFIDF, Word2Vector to create vectors on the given dataset. And them try the sentiment analysis. You may also need glove vector for the conducting analysis.
For this topic I found that this field in machine learning is called "Natural Language Questions" it's a field where machine learning models trained to detect questions in text and suggesting answer for them based on data set you are working with, check this article for more detail.
I'm looking to use Mallet to classify different documents by topics that I have defined. I know that Mallet will first determine the topics, then classify the documents but I want to skip the first step because I already have a list of topics with words associated with them. Is there any way to use pre-defined topic lists that I have created to classify documents with Mallet?
Any guidance is appreciated. Thanks!
If you're doing unsupervised learning (without training examples, i.e. docs for each topic), you cannot trivially just set the topics. The point is that the training algorithm does not know anything about the docs in advance. It just tries to separate/distribute them, based on the features you provide.
If you're doing supervised learning, topics are actually classes and you have documents for each class. Then the algorithm tries to learn which features are significant for each class. In mallet you should use the Classification module.
There are probably some fancy topic modelling ideas, which incorporate / skew the topic distributions according to specific keywords, but I don't think that's possible with Mallet.
I'm currently doing the topic modeling things (beginner)
I was thinking using mallet for some tool to get me understand this area, but, my problem is, I'd like to train a model based on, let's say, 1000 documents, to construct a model and using the model on a new single document to generate its potential topics.
But, as far as I read about mallet tutorial, it always says like this tool or API is useful on a corpus of texts, which means, it's used to find topics within several documents.
Is there a way that it can find topic on single document based on the model (or inference parameter it learned / constructed from the 1000 documents?)
Is there any other tool that can do this?
Thanks a lot!
You can refer the example code src/cc/mallet/examples/TopicModel.java which describes how to clustering and infer the new instance.
Actually when you run the simple LDA on a directory the model assigns topic proportions to each of the documents of that directory based on "an already" trained model from a part of your corpus. So, topic proportions are assigned with a certain probability to each of the documents (already ranked by the probability of appearance of that topic to that specific document).
I would like to do emotion classification on text (posts from social media e.g. tweets, facebook wall posts, youtube comments etc ...). Though I can't find a good dataset with annotated data. I'm looking for more than just data annotated with positive and negative. I'm looking for a dataset with several emotions. This could be or discrete values (ekman 6 basic emotions) or continues values (arousal-valence model). Does anyone know where I can get such a dataset, this can be from twitter, Facebook, Myspace ... as long it is from a social network
well, I think better name (or, more often used) would be Sentiment analysis (Sentiment classification) - correct? I'm not sure if social media do offer their private data (maybe some part of it). Anyway, I found this paper:
http://www.cs.cornell.edu/home/llee/papers/sentiment.pdf
They are dealing with data: http://www.cs.cornell.edu/people/pabo/movie-review-data/ from https://groups.google.com/forum/?fromgroups#!aboutgroup/rec.arts.movies.reviews.
Does it suit you? Basically, finding appropriate data is usually a big problem in ML. Often it is needed to build your own (I mean to classify a part of it manually and apply some clustering or semi-supervised learning afterwards)
If you don't find anything appropriate on the web, I'd try to contact some authors that write articles similar to your research. Maybe they will have already created datasets that will fit you...