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https://github.com/amirbar/reviews.sentiments.classifier


https://github.com/amirbar/reviews.sentiments.classifier

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README 

        
# Movies Reviews Sentiment Classifier



#### This code aims to build a classifier on movie reviews. ####

The classification is binary, meaning we aim to predict whether a review is positive or negative.



#### Step1: For each review in the dataset, convert the review to a fixed size vector. 

  1.1 Place the reviews in Data/neg & Data/pos.

  1.2 Place the word2vec representation text file as: Data/vectors.txt.

  1.3 Run InitialDatasetCreation/GenerateTrainingData.m.



#### Step2: Train the model  

  2.1 Run TrainSvmClassifier.m to output a model file.



#### Step3: Classify new examples

  3.1 Run go.m with input directory path param.

  3.2 The script will create a new "predicted.txt" file in its folder.

  3.3 Each line in the file specify  .

  

  

#### Negative Review Example:

This was so lame that I turned the DVD off...maybe halfway through. It was so weak, I couldn't even pay full enough attention to tell you how far in I made it.Though I really wanted to believe that the depiction of the young Carlito would be somewhat different, I just couldn't buy it. I don't really blame the actors, because I think it was the script that may have fallen flat. I did find myself laughing a few times, but I don't think those lines were intended to be funny.

It's only saving grace is that I bought it in a 2 DVD set and I would have paid the price I did for the original alone. This is one of those cases when they should have let the classic stand alone.



#### Positive Review Example:

Brilliant over-acting by Lesley Ann Warren. Best dramatic hobo lady I have ever seen, and love scenes in clothes warehouse are second to none. The corn on face is a classic, as good as anything in Blazing Saddles. The take on lawyers is also superb. After being accused of being a turncoat, selling out his boss, and being dishonest the lawyer of Pepto Bolt shrugs indifferently "I'm a lawyer" he says. Three funny words. Jeffrey Tambor, a favorite from the later Larry Sanders show, is fantastic here too as a mad millionaire who wants to crush the ghetto. His character is more malevolent than usual. The hospital scene, and the scene where the homeless invade a demolition site, are all-time classics. Look for the legs scene and the two big diggers fighting (one bleeds). This movie gets better each time I see it (which is quite often).