How do I use N-Gram Analysis for Sentiment Analysis ?
Once I split a sentence into Uni-Grams, Bi-Grams, Tri-Grams e.t,c.
How do I go forward from there ?
Sentiment analysis often refers to machine learning hence a possible way of doing so is to perform a machine learning algorithm where the attributes are grams.
Still, you can definitely collect some sentimental phrases/words as happy/sad tokens (depends on whether you are using uni-gram or bi-gram...) and simply count the sentences' number of occurrence of the tokens.
Vectorize the X-Grams using bag of words or any other technique and then apply classification algorithm: MaxEnt/SVM/RandomForest. N-Gram don't usually improve the results, in fact using more then 2 grams may even decrease your PR.
Related
I have various restaurant labels with me and i have some words that are unrelated to restaurants as well. like below:
vegan
vegetarian
pizza
burger
transportation
coffee
Bookstores
Oil and Lube
I have such mix of around 500 labels. I want to know is there a way pick the similar labels that are related to food choices and leave out words like oil and lube, transportation.
I tried using word2vec but, some of them have more than one word and could not figure out a right way.
Brute-force approach is to tag them manually. But, i want to know is there a way using NLP or Word2Vec to cluster all related labels together.
Word2Vec could help with this, but key factors to consider are:
How are your word-vectors trained? Using off-the-shelf vectors (like say the popular GoogleNews vectors trained on a large corpus of news stories) are unlikely to closely match the senses of these words in your domain, or include multi-word tokens like 'oil_and_lube'. But, if you have a good training corpus from your own domain, with multi-word tokens from a controlled vocabulary (like oil_and_lube) that are used in context, you might get quite good vectors for exactly the tokens you need.
The similarity of word-vectors isn't strictly 'synonymity' but often other forms of close-relation including oppositeness and other ways words can be interchangeable or be used in similar contexts. So whether or not the word-vector similarity-values provide a good threshold cutoff for your particular desired "related to food" test is something you'd have to try out & tinker around. (For example: whether words that are drop-in replacements for each other are closest to each other, or words that are common-in-the-same-topics are closest to each other, can be influenced by whether the window parameter is smaller or larger. So you could find tuning Word2Vec training parameters improve the resulting vectors for your specific needs.)
Making more recommendations for how to proceed would require more details on the training data you have available – where do these labels come from? what's the format they're in? how much do you have? – and your ultimate goals – why is it important to distinguish between restaurant- and non-restaurant- labels?
OK, thank you for the details.
In order to train on word2vec you should take into account the following facts :
You need a huge and variate text dataset. Review your training set and make sure it contains the useful data you need in order to obtain what you want.
Set one sentence/phrase per line.
For preprocessing, you need to delete punctuation and set all strings to lower case.
Do NOT lemmatize or stemmatize, because the text will be less complex!
Try different settings:
5.1 Algorithm: I used word2vec and I can say BagOfWords (BOW) provided better results, on different training sets, than SkipGram.
5.2 Number of layers: 200 layers provide good result
5.3 Vector size: Vector length = 300 is OK.
Now run the training algorithm. The, use the obtained model in order to perform different tasks. For example, in your case, for synonymy, you can compare two words (i.e. vectors) with cosine (or similarity). From my experience, cosine provides a satisfactory result: the distance between two words is given by a double between 0 and 1. Synonyms have high cosine values, you must find the limit between words which are synonyms and others that are not.
I am building an NLP pipeline and I am trying to get my head around in regards to the optimal structure. My understanding at the moment is the following:
Step1 - Text Pre-processing [a. Lowercasing, b. Stopwords removal, c. stemming, d. lemmatisation,]
Step 2 - Feature extraction
Step 3 - Classification - using the different types of classifier(linearSvC etc)
From what I read online there are several approaches in regard to feature extraction but there isn't a solid example/answer.
a. Is there a solid strategy for feature extraction ?
I read online that you can do [a. Vectorising usin ScikitLearn b. TF-IDF]
but also I read that you can use Part of Speech or word2Vec or other embedding and Name entity recognition.
b. What is the optimal process/structure of using these?
c. On the text pre-processing I am ding the processing on a text column on a df and the last modified version of it is what I use as an input in my classifier. If you do feature extraction do you do that in the same column or you create a new one and you only send to the classifier the features from that column?
Thanks so much in advance
The preprocessing pipeline depends mainly upon your problem which you are trying to solve. The use of TF-IDF, word embeddings etc. have their own restrictions and advantages.
You need to understand the problem and also the data associated with it. In order to make the best use of the data, we need to implement the proper pipeline.
Specifically for text related problems, you will find word embeddings to be very useful. TF-IDF is useful when the problem needs to be solved emphasising the words with lesser frequency. Word embeddings, on the other hand, convert the text to a N-dimensional vector which may show up similarity with some other vector. This could bring a sense of association in your data and the model can learn the best features possible.
In simple cases, we can use a bag of words representation to tokenize the texts.
So, you need to discover the best approach for your problem. If you are solving a problems which closely resembles the famous NLP problems like IMDB review classification, sentiment analysis on Twitter data, then you can find a number of approaches on the internet.
Folks,
I have searched Google for different type of papers/blogs/tutorials etc but haven't found anything helpful. I would appreciate if anyone can help me. Please note that I am not asking for code step-by-step but rather an idea/blog/paper or some tutorial.
Here's my problem statement:
Just like sentiment analysis is used for identifying positive and
negative tone of a sentence, I want to find whether a sentence is
forward-looking (future outlook) statement or not.
I do not want to use bag of words approach to sum up the number of forward-looking words/phrases such as "going forward", "in near future" or "In 5 years from now" etc. I am not sure if word2vec or doc2vec can be used. Please enlighten me.
Thanks.
It seems what you are interested in doing is finding temporal statements in texts.
Not sure of your final output, but let's assume you want to find temporal phrases or sentences which contain them.
One methodology could be the following:
Create list of temporal terms [days, years, months, now, later]
Pick only sentences with key terms
Use sentences in doc2vec model
Infer vector and use distance metric for new sentence
GMM Cluster + Limit
Distance from average
Another methodology could be:
Create list of temporal terms [days, years, months, now, later]
Do Bigram and Trigram collocation extraction
Keep relevant collocations with temporal terms
Use relevant collocations in a kind of bag-of-collocations approach
Matched binary feature vectors for relevant collocations
Train classifier to recognise higher level text
This sounds like a good case for a Bootstrapping approach if you have large amounts of texts.
Both are semi-supervised really, since there is some need for finding initial temporal terms, but even that could be automated using a word2vec scheme and bootstrapping
I'm trying to develop an program in Python that can process raw chat data and cluster sentences with similar intents so they can be used as training examples to build a new chatbot. The goal is to make it as quick and automatic (i.e. no parameters to enter manually) as possible.
1- For feature extraction, I tokenize each sentence, stem its words and vectorize it using Sklearn's TfidfVectorizer.
2- Then I perform clustering on those sentence vectors with Sklearn's DBSCAN. I chose this clustering algorithm because it doesn't require the user to specify the desired number of clusters (like the k parameter in k-means). It throws away a lot of sentences (considering them as outliers), but at least its clusters are homogeneous.
The overall algorithm works on relatively small datasets (10000 sentences) and generates meaningful clusters, but there are a few issues:
On large datasets (e.g. 800000 sentences), DBSCAN fails because it requires too much memory, even with parallel processing on a powerful machine in the cloud. I need a less computationally-expensive method, but I can't find another algorithm that doesn't make weird and heterogeneous sentence clusters. What other options are there? What algorithm can handle large amounts of high-dimensional data?
The clusters that are generated by DBSCAN are sentences that have similar wording (due to my feature extraction method), but the targeted words don't always represent intents. How can I improve my feature extraction so it better captures the intent of a sentence? I tried Doc2vec but it didn't seem to work well with small datasets made of documents that are the size of a sentence...
A standard implementation of DBSCAN is supposed to need only O(n) memory. You cannot get lower than this memory requirement. But I read somewhere that sklearn's DBSCAN actually uses O(n²) memory, so it is not the optimal implementation. You may need to implement this yourself then, to use less memory.
Don't expect these methods to be able to cluster "by intent". There is no way an unsupervised algorithm can infer what is intended. Most likely, the clusters will just be based on a few key words. But this could be whether people say "hi" or "hello". From an unsupervised point of view, this distinction gives two nice clusters (and some noise, maybe also another cluster "hola").
I suggest to train a supervised feature extraction based on a subset where you label the "intent".
I am embarking upon a NLP project for sentiment analysis.
I have successfully installed NLTK for python (seems like a great piece of software for this). However,I am having trouble understanding how it can be used to accomplish my task.
Here is my task:
I start with one long piece of data (lets say several hundred tweets on the subject of the UK election from their webservice)
I would like to break this up into sentences (or info no longer than 100 or so chars) (I guess i can just do this in python??)
Then to search through all the sentences for specific instances within that sentence e.g. "David Cameron"
Then I would like to check for positive/negative sentiment in each sentence and count them accordingly
NB: I am not really worried too much about accuracy because my data sets are large and also not worried too much about sarcasm.
Here are the troubles I am having:
All the data sets I can find e.g. the corpus movie review data that comes with NLTK arent in webservice format. It looks like this has had some processing done already. As far as I can see the processing (by stanford) was done with WEKA. Is it not possible for NLTK to do all this on its own? Here all the data sets have already been organised into positive/negative already e.g. polarity dataset http://www.cs.cornell.edu/People/pabo/movie-review-data/ How is this done? (to organise the sentences by sentiment, is it definitely WEKA? or something else?)
I am not sure I understand why WEKA and NLTK would be used together. Seems like they do much the same thing. If im processing the data with WEKA first to find sentiment why would I need NLTK? Is it possible to explain why this might be necessary?
I have found a few scripts that get somewhat near this task, but all are using the same pre-processed data. Is it not possible to process this data myself to find sentiment in sentences rather than using the data samples given in the link?
Any help is much appreciated and will save me much hair!
Cheers Ke
The movie review data has already been marked by humans as being positive or negative (the person who made the review gave the movie a rating which is used to determine polarity). These gold standard labels allow you to train a classifier, which you could then use for other movie reviews. You could train a classifier in NLTK with that data, but applying the results to election tweets might be less accurate than randomly guessing positive or negative. Alternatively, you can go through and label a few thousand tweets yourself as positive or negative and use this as your training set.
For a description of using Naive Bayes for sentiment analysis with NLTK: http://streamhacker.com/2010/05/10/text-classification-sentiment-analysis-naive-bayes-classifier/
Then in that code, instead of using the movie corpus, use your own data to calculate word counts (in the word_feats method).
Why dont you use WSD. Use Disambiguation tool to find senses. and use map polarity to the senses instead of word. In this case you will get a bit more accurate results as compared to word index polarity.