Excluding mentions when counting words in a database of tweets
I need to count the total number of words in a csv file which is my database of tweets. How do I exclude mentions (all the characters starting with @) from counting?
Here's what I have for now:
words = open('file_directory', 'r').read() words = words.replace('.',' ').replace('!',' ').replace('?',' ').replace(';',' ').lower() words = words.split() print(words) print("For the query 'lala' we have %s" %len(words))
I'm new to Python, so your help would be really appreciated! Thanks!
You could use a list comprehension
new_words = [i for i in words if i != '@'] print("For the query 'lala' we have %s" %len(new_words))
Analyze Word Frequency Counts Using Twitter Data and Tweepy in , Flatten list of words in clean tweets Counter(all_words_nsw_nc) counts_nsw_nc.most_common(15) Words Found in Tweets (Without Stop or Collection� Doing this will mute Tweets notifications that mention that account, but won’t mute the account itself. Words, phrases, usernames, emojis, and hashtags up to the max character count can be muted. Muting is possible across all Twitter-supported languages. Muting is set to a default time period of Forever. Instructions on how to adjust the mute
I would suggest using regex to remove word starting with
words = re.sub(r'@\w ', '', words)
Mining Twitter Data with Python (Part 3: Term Frequencies) – Marco , As you can see, the most frequent words (or should I say, tokens), are not exactly Count terms only (no hashtags, no mentions) What you can do is simply to process your tweets after you've downloaded them (without I'm working with a large data file, 80 000 tweets or so, which is in an SQL database. This parameter will prevent replies from appearing in the returned timeline. Using exclude_replies with the count parameter will mean you will receive up-to count tweets — this is because the count parameter retrieves that many tweets before filtering out retweets and replies. This parameter is only supported for JSON and XML responses
If I interpret your question right, you want to filter out any words that have
@ as their first character? Here's how you can do it with a list comprehension:
# [...] words = words.split() words = [word for word in words if not word.startswith("@")] # [...]
Extracting Twitter Data, Pre-Processing and Sentiment Analysis , Unlike other social platforms, almost every user's tweets are Because that's a must, nowadays people don't tweet without emojis, preprocessing the colon symbol left remain after #removing mentions #check tokens against stop words , emoticons and punctuations count=200, include_rts=False,. Basically, method implies the counting of the most mentioned terms in the poster tweets in the Twitter social network. The method is known in the domain of data analysis for the social network as
What is the best way to gather a full Twitter dataset for a specific , I need all tweets containing a specific hashtag in a certain period of time. This means you'll only get a fraction of past mentions and it works to the best of its as well as metrics such as social media reach, follower count of the author, etc. tweets between two dates using tweepy from all users? how to do this without� This part can have multiple relationships for a single tweet, as you can mention multiple people in one tweet. Then if neither a retweet or a mention is present, print the author and the tweet. End goal of this is to simple parse a JSON file and return a CSV file that I can then analyze. I will not be loading this into any type of database.
Twitter Sentiment Analysis with Machine Learning, Using sentiment analysis tools to analyze opinions in Twitter data can help reach a broad audience and connect with customers without intermediaries. With sentiment analysis, you can monitor brand mentions on Twitter in First, we were able to count the number of positive and negative mentions for� Yes, Commun.it has a mention counter for Twitter account. Following mentions of your Twitter account, your brand or your product is an effective way of both monitoring your reputation and popularity and of engaging back with those who mention you.
Twitter Data Extraction using Python. Twitter is a gold mine of data. Unlike other social platforms, almost every user’s tweets are completely public and pullable. This is a huge plus if you’re trying to get a large amount of data to run analytics on.