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I would like to do some NLP analysis for a string column in pyspark dataframe.

df:

 year month u_id rating_score p_id review
 2010 09    tvwe  1           p_5  I do not like it because its size is not for me.  
 2011 11    frsa  1           p_7  I am allergic to the peanut elements.  
 2015 5     ybfd  1           p_2  It is a repeated one, please no more.
 2016 7     tbfb  2           p_2  It is not good for my oil hair.

Each p_id represents an item. Each u_id may have some reviews for each item. The review can be several words, one sentence or a paragraph or even emoji.

I would like to find the root reasons that the items are rated low or high. For example, how many "u_id"s complain the issue of item's size, chemical elements allergy or others, which are relevant to the items' features.

From How to iterate over rows in a DataFrame in Pandas, I learn that it is more efficient to transform the dataframe to numpy array then use vectorization to do NLP analysis.

I am trying to use SparkNLP to extract adjectives and noun phrase for each comment by year, month, u_id, p_id.

I am not sure how to apply the numpy vectorization to do this very efficiently.

My py3 code:

from sparknlp.pretrained import PretrainedPipeline
df = spark.sql('select year, month, u_id, p_id, comment from MY_DF where rating_score = 1 and isnull(comment) = false')
import numpy as np

trainseries = df['comment'].apply(lambda x : np.array(x.toArray())).as_matrix().reshape(-1,1)

text = np.apply_along_axis(lambda x : x[0], 1, trainseries) # TypeError: 'Column' object is not callable

pipeline_dl = PretrainedPipeline('explain_document_dl', lang='en') # 
result = pipeline_dl.fullAnnotate(text)

The code does not work. I also need to keep the other columns (e.g. year, month, u_id, p_id) in the vectorization and assure that the NLP analysis results can be aligned with year, month, u_id, p_id well.

I do not like this How to convert a pyspark dataframe column to numpy array because collect() is too slow.

Thanks

user3448011
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1 Answers1

1

IIUC, you don't need Numpy(Spark handles vectorization internally), just do transform and then select and filter the proper information from the resulting dataframe:

from sparknlp.pretrained import PretrainedPipeline

df = spark.sql('select year, month, u_id, p_id, comment from MY_DF where rating_score = 1 and isnull(comment) = false')

df1 = df.withColumnRenamed('comment', 'text')

pipeline_dl = PretrainedPipeline('explain_document_dl', lang='en')

result = pipeline_dl.transform(df1)

df_new = result.selectExpr(
  *df1.columns,
  'transform(filter(pos, p -> p.result rlike "^(?:NN|JJ)"), x -> x.metadata.word) as words'
)

Output:

df_new.show(10,0)
+-----+-----+----+------------+----+------------------------------------------------+----------------------------+
|years|month|u_id|rating_score|p_id|text                                            |words                       |
+-----+-----+----+------------+----+------------------------------------------------+----------------------------+
|2010 |09   |tvwe|1           |p_5 |I do not like it because its size is not for me.|[size]                      |
|2011 |11   |frsa|1           |p_7 |I am allergic to the peanut elements.           |[allergic, peanut, elements]|
|2015 |5    |ybfd|1           |p_2 |It is a repeated one, please no more.           |[more]                      |
|2016 |7    |tbfb|2           |p_2 |It is not good for my oil hair.                 |[good, oil, hair]           |
+-----+-----+----+------------+----+------------------------------------------------+----------------------------+

Note:

(1) result = pipeline.fullAnnotate(df,'comment') is a shortcut of renaming comment to text and then doing pipeline.transform(df1). the first argument of fullAnnotate can be a DataFrame, List or a String.

(2) the list of POS tags from https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html

jxc
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