Tidy up preprocess.py with pandas

[abdulfatir]

In preprocess_csv function in preprocess.py (link), pandas can be used to parse the csv more efficiently and with way less code. The machine I was using while developing the project did not have pandas installed.

[GongQin721]

I run your codes,it happend [Errno 2] No such file or directory: '../train-processed-freqdist.pkl',can you solve my problem?Thank you

[abdulfatir]

@GongQin721 This is off-topic. Please read the Readme properly.

[GongQin721]

OK ,thank you very much!

[chaiitanyasangani88]

Can you help me with headers of the csv, if any? If not, some idea about the structure of csv would be of great help.

[abdulfatir]

Hi @chaiitanyasangani88

The csv structure is in the Dataset Information section:

We use and compare various different methods for sentiment analysis on tweets (a binary classification problem). The training dataset is expected to be a csv file of type tweet_id,sentiment,tweet where the tweet_id is a unique integer identifying the tweet, sentiment is either 1 (positive) or 0 (negative), and tweet is the tweet enclosed in "". Similarly, the test dataset is a csv file of type tweet_id,tweet. Please note that csv headers are not expected and should be removed from the training and test datasets.

[Carolinecrl]

In your lstm.py code, these '.csv' and '.pkl' files are showed asFREQ_DIST_FILE = '../train-processed-freqdist.pkl' ,TRAIN_PROCESSED_FILE = '../train-processed.csv' and so on. I wonder how can I process these file from 'positive-words.txt' and 'negative-words.txt' in dataset. Could you please help me with problems above?

[abdulfatir]

@Carolinecrl

'positive-words.txt' and 'negative-words.txt' are not the dataset. They're just for the baseline. The dataset is not included in the repo.

[16L31A0575n1]

in stats.py which csv file should be sent train or test or any another sample (random )one.