I have searched the internet and there is more or less the same sentiment analysis of a sentence i.e Positive, Negative or Neutral. I want to build a sentiment analyzer that look for the following sentiments/emotions for a sentence.
happy , sad , angry , disaapointed , surprised, proud, in love, scared
It would be nice for you to explore a bit further what you tried so far and more in details of what you want to do. So, I'm answering this based on the assumption that you want to work with an emotion-based Sentiment Analysis. Actually there is an area of research that focus on identifying emotion from text.
In many cases, the problem is still treated as a multiclass classification problem, but instead of predicting sentiment polarity (positive, negative or neutral), people try to find emotions. The existing emotions vary in different research and different annotated data, but in general it looks like the ones you mentioned.
Your best chance to understand this area further is to look for papers and existing datasets. I'll list a few here for you and the emotions they work with:
An Analysis of Annotated Corpora for Emotion Classification in Text. Literature review of methods and corpus for such analysis.
Emotion Detection and Analysis on Social Media. Happiness, Sadness, Fear, Anger, Surprise and Disgust
This dataset is a good source for training data. Sadness, Enthusiasm, Neutral, Worry, Love, Fun, Hate, Happiness,
Related
I am given a task of classifying a given news text data into one of the following 5 categories - Business, Sports, Entertainment, Tech and Politics
About the data I am using:
Consists of text data labeled as one of the 5 types of news statement (Bcc news data)
I am currently using NLP with nltk module to calculate the frequency distribution of every word in the training data with respect to each category(except the stopwords).
Then I classify the new data by calculating the sum of weights of all the words with respect to each of those 5 categories. The class with the most weight is returned as the output.
Heres the actual code.
This algorithm does predict new data accurately but I am interested to know about some other simple algorithms that I can implement to achieve better results. I have used Naive Bayes algorithm to classify data into two classes (spam or not spam etc) and would like to know how to implement it for multiclass classification if it is a feasible solution.
Thank you.
In classification, and especially in text classification, choosing the right machine learning algorithm often comes after selecting the right features. Features are domain dependent, require knowledge about the data, but good quality leads to better systems quicker than tuning or selecting algorithms and parameters.
In your case you can either go to word embeddings as already said, but you can also design your own custom features that you think will help in discriminating classes (whatever the number of classes is). For instance, how do you think a spam e-mail is often presented ? A lot of mistakes, syntaxic inversion, bad traduction, punctuation, slang words... A lot of possibilities ! Try to think about your case with sport, business, news etc.
You should try some new ways of creating/combining features and then choose the best algorithm. Also, have a look at other weighting methods than term frequencies, like tf-idf.
Since your dealing with words I would propose word embedding, that gives more insights into relationship/meaning of words W.R.T your dataset, thus much better classifications.
If you are looking for other implementations of classification you check my sample codes here , these models from scikit-learn can easily handle multiclasses, take a look here at documentation of scikit-learn.
If you want a framework around these classification that is easy to use you can check out my rasa-nlu, it uses spacy_sklearn model, sample implementation code is here. All you have to do is to prepare the dataset in a given format and just train the model.
if you want more intelligence then you can check out my keras implementation here, it uses CNN for text classification.
Hope this helps.
I am about to start a project where my final goal is to classify short texts into classes: "may be interested in visiting place X" : "not interested or neutral". Place is described by set of keywords (e.g. meals or types of miles like "chinese food"). So ideally I need some approach to model desire of user based on short text analysis - and then classify based on a desire score or desire probability - is there any state-of-the-art in this field ? Thank you
This problem is exactly the same as sentiment analysis of texts. But, instead of the traditional binary classification, you seem to have a "neutral" opinion. State-of-the-art in sentiment analysis is highly domain-dependent. Techniques that have excelled in classifying movies do not perform as well on commercial products, for example.
Additionally, even the feature-selection is highly domain-dependent. For example, unigrams work well for movie review classification, but a combination of unigrams and bigrams perform better for classifying twitter texts.
My best advice is to "play around" with different features. Since you are looking at short texts, twitter is probably a good motivational example. I would start with unigrams and bigrams as my features. The exact algorithm is not very important. SVM usually performs very well with correct parameter tuning. Use a small amount of held-out data for tuning these parameters before experimenting on bigger datasets.
The more interesting portion of this problem is the ranking! A "purity score" has been recently used for this purpose in the following papers (and I'd say they are pretty state-of-the-art):
Sentiment summarization: evaluating and learning user preferences. Lerman, Blair-Goldensohn and McDonald. EACL. 2009.
The viability of web-derived polarity lexicons. Velikovich, Blair-Goldensohn, Hannan and McDonald. NAACL. 2010.
Can anybody please explain multitask learning in simple and intuitive way? May be some real
world problem would be useful.Mostly, these days i am seeing many people are using it for natural language processing tasks.
Let's say you've built a sentiment classifier for a few different domains. Say, movies, music DVDs, and electronics. These are easy to build high quality classifiers for, because there is tons of training data that you've scraped from Amazon. Along with each classifier, you also build a similarity detector that will tell you for a given piece of text, how similar it was to the dataset each of the classifiers was trained on.
Now you want to find the sentiment of some text from an unknown domain or one in which there isn't such a great dataset to train on. Well, how about we take a similarity weighted combination of the classifications from the three high quality classifiers we already have. If we are trying to classify a dish washer review (there is no giant corpus of dish washer reviews, unfortunately), it's probably most similar to electronics, and so the electronics classifier will be given the most weight. On the other hand, if we are trying to classify a review of a TV show, probably the movies classifier will do the best job.
I want to classify text messages into several categories like, "relation building", "coordination", "information sharing", "knowledge sharing" & "conflict resolution". I am using NLTK library to process these data. I would like to know which classifier, in nltk, is better for this particular multi-class classification problem.
I am planning to use Naive Bayes Classification, is it advisable?
Naive Bayes is the simplest and easy to understand classifier and for that reason it's nice to use. Decision Trees with a beam search to find the best classification are not significantly harder to understand and are usually a bit better. MaxEnt and SVM tend be more complex, and SVM requires some tuning to get right.
Most important is the choice of features + the amount/quality of data you provide!
With your problem, I would focus first on ensuring you have a good training/testing dataset and also choose good features. Since you are asking this question you haven't had much experience with machine learning for NLP, so I'd say start of easy with Naive Bayes as it doesn't use complex features- you can just tokenize and count word occurrences.
EDIT:
The question How do you find the subject of a sentence? and my answer are also worth looking at.
Yes, Training a Naive Bayes Classifier for each category and then labeling each message to a class based on which Classifier provides the highest score is a standard first approach to problems like this. There are more sophisticated single class classifier algorithms which you could substitute in for Naive Bayes if you find performance inadequate, such as a Support Vector Machine ( Which I believe is available in NLTK via a Weka plug in, but not positive). Unless you can think of anything specific in this problem domain that would make Naieve Bayes especially unsuitable, its ofen the go-to "first try" for a lot of projects.
The other NLTK classifier I would consider trying would be MaxEnt as I believe it natively handles multiclass classification. (Though the multiple binary classifer approach is very standard and common as well). In any case the most important thing is to collect a very large corpus of properly tagged text messages.
If by "Text Messages" you are referring to actual cell phone text messages these tend to be very short and the language is very informal and varied, I think feature selection may end up being a larger factor in determining accuracy than classifier choice for you. For example, using a Stemmer or Lemmatizer that understands common abbreviations and idioms used, tagging part of speech or chunking , entity extraction, extracting probably relationships between terms may provide more bang than using more complex classifiers.
This paper talks about classifying Facebook status messages based on sentiment, which has some of the same issues, and may provide some insights into this. The links is to a google cache because I'm having problems w/ the original site:
http://docs.google.com/viewer?a=v&q=cache:_AeBYp6i1ooJ:nlp.stanford.edu/courses/cs224n/2010/reports/ssoriajr-kanej.pdf+maxent+classifier+multiple+classes&hl=en&gl=us&pid=bl&srcid=ADGEESi-eZHTZCQPo7AlcnaFdUws9nSN1P6X0BVmHjtlpKYGQnj7dtyHmXLSONa9Q9ziAQjliJnR8yD1Z-0WIpOjcmYbWO2zcB6z4RzkIhYI_Dfzx2WqU4jy2Le4wrEQv0yZp_QZyHQN&sig=AHIEtbQN4J_XciVhVI60oyrPb4164u681w&pli=1
It appears that the simplest, naivest way to do basic sentiment analysis is with a Bayesian classifier (confirmed by what I'm finding here on SO). Any counter-arguments or other suggestions?
A Bayesian classifier with a bag of words representation is the simplest statistical method. You can get significantly better results by moving to more advanced classifiers and feature representation, at the cost of more complexity.
Statistical methods aren't the only game in town. Rule based methods that have more understanding of the structure of the text are the other main option. From what I have seen, these don't actually perform as well as statistical methods.
I recommend Manning and Schütze's Foundations of Statistical Natural Language Processing chapter 16, Text Categorization.
I can't think of a simpler, more naive way to do Sentiment Analysis, but you might consider using a Support Vector Machine instead of Naive Bayes (in some machine learning toolkits, this can be a drop-in replacement). Have a look at "Thumbs up? Sentiment Classification using Machine Learning Techniques" by Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan which was one of the earliest papers on these techniques, and gives a good table of accuracy results on a family of related techniques, none of which are any more complicated (from a client perspective) than any of the others.
Building upon the answer provided by Ken above, there is another paper
"Sentiment analysis using support vector machines with diverse information sources" by Tony and Niger,
which looks at assigning more features than just a bag of words used by Pang and Lee. Here, they leverage wordnet to determine semantic differentiation of adjectives, and proximity of the sentiment towards the topic in the text, as additional features for SVM. They show better results than previous attempts to classify text based on sentiment.