I faced some issues when I tried to use textblob to analyze 10million data. For the data, I’ve also used Vader packages to handle the same data, it worked well. My original code was: def textblob_polarity(text): polarity =  for mess in text: mood = TextBlob(mess) polarity.append(mood.sentiment.polarity) return polarity textblob_polarity = textblob_polarity(data[‘body’]) data[‘textblob_polarity’] = textblob_polarity ..
I came across this code for BERT sentiment analysis where the unused layers are removed, Update trainable vars/trainable weights are added and I am looking for a documentation which shows what are the different layers in bert, how can we remove the unused layers, add weights etc. However, I am unable to find any documentation ..
I am a graduate student doing some research on sentiment analysis of tweets. And for tweets I have been using twint for scraping tweets from selected cities where I was getting more tweets when compared to scraping tweets for the whole world for the same hashtag for a duration of 5 years from 2010 to ..
I’m doing sentiment analysis on paragraphs. I split it into sentences. Based on context categorized each sentences to specific groups and also obtained their polarity scores. How can I match a paragraph to single group along with polarity? Source: Python..
I am working on sentiment analysis where after preprocessing the dataset of strings i have to change them into numbers to perform logistic regression. It took almost 10 minutes to preprocess the dataset of 400,000 examples token by token. So i saved the preprocessed dataset as a CSV file. Opened the file and use TfidVectoriser ..
I have used vader library for labeling of amazon’s reviews but it doesn’t handle these types of reviews "No problems with it and does job well. Using it for Apple TV and works great. I would buy again no problem". This is positive sentence but the code label it as negative. How can I handle ..
I’m currently using Spacy for a sentiment analysis. The input I received looked like this: zweite, None, ADJ wunderschöne, 0.7048, ADJ Testdatei, None, NOUN Diese, None, PRON brauchen, None, VERB wir, None, PRON I managed to extract only the numbers and nones. I wanted to put them into a list, so I could in a ..
Here is a snippet of the code: # input for term to be searched and how many tweets to search searchTerm = input("Enter Keyword/Tag to search about: ") NoOfTerms = int(input("Enter how many tweets to search: ")) # searching for tweets self.tweets = tweepy.Cursor(api.search, q=searchTerm, lang = "en").items(NoOfTerms) Source: Python..
This is the error info+ most recent call KeyError Traceback (most recent call last) D:pythonlibsite-packagespandascoreindexesbase.py in get_loc(self, key, method, tolerance) 3079 try: -> 3080 return self._engine.get_loc(casted_key) 3081 except KeyError as err: pandas_libsindex.pyx in pandas._libs.index.IndexEngine.get_loc() pandas_libsindex.pyx in pandas._libs.index.IndexEngine.get_loc() pandas_libshashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() pandas_libshashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() KeyError: 1 The above exception was the direct cause of the following ..
The code below fails for me when I am trying to load the flair en-sentiment model. I am new to sentiment analysis and I’ve been following a tutorial but I can’t proceed as I keep getting this error. Code: import requests from flair.models import TextClassifier from flair.data import Sentence classifier = TextClassifier.load(‘en-sentiment’) The error I ..