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I want to know what are the appropriate tools for each step to analyse sentiment :
removing stopwords,
stemming,
Vector Representation of Text,
feature selection,
classification,
how to pass from Vector Representation of Text to feature selection , is there any step to follow and an examlpe which describe those steps
You can take a look at Bing Liu's tutorial on sentiment analysis here, and Pang & Lee's survey here. They cover the different aspects of sentiment analysis and common approaches.
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I'm interested in applying machine learning algorithms for SAT solving procedures. Current trends on SAT solvers seems they use CDCL procedures.
Specifically, is there any small example to illustrate the idea?
The closest work I'm aware of is by Selsam et al., "Learning a SAT Solver from Single-Bit Supervision." See https://arxiv.org/abs/1802.03685
You should also look into Selsam's PhD thesis on this topic as well https://searchworks.stanford.edu/view/13250178, which has lots of other references.
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I am trying to develop a system(machine learning model) to identify whether the land is agricultural or paddy (dry land or wet-land) and has undergone changes over a period of time.
Can anyone suggest suitable dataset which I can use for training purposes?
after a few research i have got some datasets websites Please do check it:
https://data.world/datasets/land
https://www.researchgate.net/publication/317914265_A_global_dataset_of_crowdsourced_land_cover_and_land_use_reference_data
https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/gdj3.32
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I need a dataset for my future project based on image processing, hand recognition. I need a minimum of 5 hand position and orientation and a minumim of 200 images per set.
I want to know if there are websites where I can find a great variety of datasets.
I recommend you to search here: http://homepages.inf.ed.ac.uk/rbf/CVonline/Imagedbase.htm#gesture.
I needed this for a project too and I found a lot of variations here. It also depends on what kind of gestures you are looking for, but I think you will find here what you need.
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I am a sort of newbie to NLP world.
But anyway, I have just started my NLP project.
My task is about inferring hidden sentence in a paragraph.
Let me show you an example question.
a multiple choice question about inferring a clause in the blank
I want my machine learning model to extract some meaningful phrase from the given text(in above image, a paragraph)
I know that my question sounds quite ambiguous for you all. I just want to know even a small clue.
Thank you for your response in advance.
Skip-thought vectors are a system for predicting sentences from a context, by essentially constructing sentence-wide vectors. Might be useful, especially so in combination with context2vec if you want to build a custom model.
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I have a DataSet of words and texts and I want to make clusters (by K-means )or any other unsupervised/supervised learning method to distinguish words for example , the word 'John' will be classified as a name(and will be clustered with other person names) , 'brazil' as a place and etc...
Is there a model that I can use to solve the problem.
I have Heard of N-grams but I dont know how to plot the Ngrams probability on a x,y plot or such
P.S if you have any examples that will be wonderful
How about word2vec and embeddings?
https://deeplearning4j.org/word2vec