How to detect if one tweet is agreeing with another
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I want to detect tweet text agreement. Suppose someone posts some subjective opinion in twitter. Other users will post reply either agreeing or opposing the original tweet. I want to estimate the amount of agreement. Is there any algorithm/library in any language to do that or any labeled dataset?
nlp sentiment-analysis twitter
asked Jan 19 '18 at 13:06
RakibRakib
145110
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bumped to the homepage by Community♦ 15 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I want to detect tweet text agreement. Suppose someone posts some subjective opinion in twitter. Other users will post reply either agreeing or opposing the original tweet. I want to estimate the amount of agreement. Is there any algorithm/library in any language to do that or any labeled dataset?
nlp sentiment-analysis twitter
asked Jan 19 '18 at 13:06
RakibRakib
145110
$endgroup$
bumped to the homepage by Community♦ 15 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I want to detect tweet text agreement. Suppose someone posts some subjective opinion in twitter. Other users will post reply either agreeing or opposing the original tweet. I want to estimate the amount of agreement. Is there any algorithm/library in any language to do that or any labeled dataset?
nlp sentiment-analysis twitter
asked Jan 19 '18 at 13:06
RakibRakib
145110
$endgroup$
I want to detect tweet text agreement. Suppose someone posts some subjective opinion in twitter. Other users will post reply either agreeing or opposing the original tweet. I want to estimate the amount of agreement. Is there any algorithm/library in any language to do that or any labeled dataset?
nlp sentiment-analysis twitter
nlp sentiment-analysis twitter
asked Jan 19 '18 at 13:06
RakibRakib
145110
asked Jan 19 '18 at 13:06
RakibRakib
145110
edited Jan 19 '18 at 14:04
Rakib
asked Jan 19 '18 at 13:06
RakibRakib
145110
asked Jan 19 '18 at 13:06
RakibRakib
145110
asked Jan 19 '18 at 13:06
RakibRakib
145110
145110
bumped to the homepage by Community♦ 15 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 15 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
Not sure there is anything for that, you could check
- is it a verbatim retweet
- does it have the same sentiment
- is the edit distance low
Or you can train your own model, where you label (agree) by hand and then build features.
answered Jan 19 '18 at 13:39
Dirk NachbarDirk Nachbar
23914
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add a comment |
$begingroup$
Sure there can be more complicated approach but if you are dealing with raw tweets, I think problem is twofolds;
- Topic Discovery
You first need to find out what the tweet is talking about. It will be much easier task if you can skip this, given that you are looking at tweets with specific tags; that you know what the tweet is pertaining to. Otherwise, you can use LDA or gensim library in Python.
- Sentiment Analysis
This is much easier task. For each topic, for all tweets, associate tweets with probability of positive / negative and also, you could scale this by certainty. This could be using out of box solution such as from nltk.
This github repo seems to be doing what you are intending to do, and could get some inspiration.
https://github.com/nagarmayank/twitter_sentiment_analysis
answered Jan 19 '18 at 15:17
won782won782
1513
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2 Answers
2
active
oldest
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2 Answers
2
active
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$begingroup$
Not sure there is anything for that, you could check
- is it a verbatim retweet
- does it have the same sentiment
- is the edit distance low
Or you can train your own model, where you label (agree) by hand and then build features.
answered Jan 19 '18 at 13:39
Dirk NachbarDirk Nachbar
23914
$endgroup$
add a comment |
$begingroup$
Not sure there is anything for that, you could check
- is it a verbatim retweet
- does it have the same sentiment
- is the edit distance low
Or you can train your own model, where you label (agree) by hand and then build features.
answered Jan 19 '18 at 13:39
Dirk NachbarDirk Nachbar
23914
$endgroup$
add a comment |
$begingroup$
Not sure there is anything for that, you could check
- is it a verbatim retweet
- does it have the same sentiment
- is the edit distance low
Or you can train your own model, where you label (agree) by hand and then build features.
answered Jan 19 '18 at 13:39
Dirk NachbarDirk Nachbar
23914
$endgroup$
Not sure there is anything for that, you could check
- is it a verbatim retweet
- does it have the same sentiment
- is the edit distance low
Or you can train your own model, where you label (agree) by hand and then build features.
answered Jan 19 '18 at 13:39
Dirk NachbarDirk Nachbar
23914
answered Jan 19 '18 at 13:39
Dirk NachbarDirk Nachbar
23914
answered Jan 19 '18 at 13:39
Dirk NachbarDirk Nachbar
23914
answered Jan 19 '18 at 13:39
Dirk NachbarDirk Nachbar
23914
23914
add a comment |
add a comment |
$begingroup$
Sure there can be more complicated approach but if you are dealing with raw tweets, I think problem is twofolds;
- Topic Discovery
You first need to find out what the tweet is talking about. It will be much easier task if you can skip this, given that you are looking at tweets with specific tags; that you know what the tweet is pertaining to. Otherwise, you can use LDA or gensim library in Python.
- Sentiment Analysis
This is much easier task. For each topic, for all tweets, associate tweets with probability of positive / negative and also, you could scale this by certainty. This could be using out of box solution such as from nltk.
This github repo seems to be doing what you are intending to do, and could get some inspiration.
https://github.com/nagarmayank/twitter_sentiment_analysis
answered Jan 19 '18 at 15:17
won782won782
1513
$endgroup$
add a comment |
$begingroup$
Sure there can be more complicated approach but if you are dealing with raw tweets, I think problem is twofolds;
- Topic Discovery
You first need to find out what the tweet is talking about. It will be much easier task if you can skip this, given that you are looking at tweets with specific tags; that you know what the tweet is pertaining to. Otherwise, you can use LDA or gensim library in Python.
- Sentiment Analysis
This is much easier task. For each topic, for all tweets, associate tweets with probability of positive / negative and also, you could scale this by certainty. This could be using out of box solution such as from nltk.
This github repo seems to be doing what you are intending to do, and could get some inspiration.
https://github.com/nagarmayank/twitter_sentiment_analysis
answered Jan 19 '18 at 15:17
won782won782
1513
$endgroup$
add a comment |
$begingroup$
Sure there can be more complicated approach but if you are dealing with raw tweets, I think problem is twofolds;
- Topic Discovery
You first need to find out what the tweet is talking about. It will be much easier task if you can skip this, given that you are looking at tweets with specific tags; that you know what the tweet is pertaining to. Otherwise, you can use LDA or gensim library in Python.
- Sentiment Analysis
This is much easier task. For each topic, for all tweets, associate tweets with probability of positive / negative and also, you could scale this by certainty. This could be using out of box solution such as from nltk.
This github repo seems to be doing what you are intending to do, and could get some inspiration.
https://github.com/nagarmayank/twitter_sentiment_analysis
answered Jan 19 '18 at 15:17
won782won782
1513
$endgroup$
Sure there can be more complicated approach but if you are dealing with raw tweets, I think problem is twofolds;
- Topic Discovery
You first need to find out what the tweet is talking about. It will be much easier task if you can skip this, given that you are looking at tweets with specific tags; that you know what the tweet is pertaining to. Otherwise, you can use LDA or gensim library in Python.
- Sentiment Analysis
This is much easier task. For each topic, for all tweets, associate tweets with probability of positive / negative and also, you could scale this by certainty. This could be using out of box solution such as from nltk.
This github repo seems to be doing what you are intending to do, and could get some inspiration.
https://github.com/nagarmayank/twitter_sentiment_analysis
answered Jan 19 '18 at 15:17
won782won782
1513
answered Jan 19 '18 at 15:17
won782won782
1513
answered Jan 19 '18 at 15:17
won782won782
1513
answered Jan 19 '18 at 15:17
won782won782
1513
1513
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add a comment |
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