Unsupervised sentiment Analysis using doc2vec

Folks,

I have searched Google for different type of papers/blogs/tutorials etc but haven't found anything helpful. I would appreciate if anyone can help me. Please note that I am not asking for code step-by-step but rather an idea/blog/paper or some tutorial.

Here's my problem statement:

Just like sentiment analysis is used for identifying positive and
negative tone of a sentence, I want to find whether a sentence is
forward-looking (future outlook) statement or not.

I do not want to use bag of words approach to sum up the number of forward-looking words/phrases such as "going forward", "in near future" or "In 5 years from now" etc. I am not sure if word2vec or doc2vec can be used. Please enlighten me.

Thanks.

asked Nov 9 '18 at 20:32

sgokhales

35.2k26106140

Why don't you want to use a bag-of-words technique based on words/phrases that appear in such statements? It might work well! Similarly, some approach using word2vec/doc2vec embeddings might prove helpful – you'd have to try it. What have you tried so far? What kind of training dataset do you have, or expect to be able to create?
– gojomo
Nov 10 '18 at 0:54

add a comment |

Folks,

Here's my problem statement:

Just like sentiment analysis is used for identifying positive and
negative tone of a sentence, I want to find whether a sentence is
forward-looking (future outlook) statement or not.

Thanks.

asked Nov 9 '18 at 20:32

sgokhales

35.2k26106140

Why don't you want to use a bag-of-words technique based on words/phrases that appear in such statements? It might work well! Similarly, some approach using word2vec/doc2vec embeddings might prove helpful – you'd have to try it. What have you tried so far? What kind of training dataset do you have, or expect to be able to create?
– gojomo
Nov 10 '18 at 0:54

add a comment |

Folks,

Here's my problem statement:

Just like sentiment analysis is used for identifying positive and
negative tone of a sentence, I want to find whether a sentence is
forward-looking (future outlook) statement or not.

Thanks.

asked Nov 9 '18 at 20:32

sgokhales

35.2k26106140

Folks,

Here's my problem statement:

Just like sentiment analysis is used for identifying positive and
negative tone of a sentence, I want to find whether a sentence is
forward-looking (future outlook) statement or not.

Thanks.

nlp gensim word2vec sentiment-analysis doc2vec

asked Nov 9 '18 at 20:32

sgokhales

35.2k26106140

asked Nov 9 '18 at 20:32

sgokhales

35.2k26106140

asked Nov 9 '18 at 20:32

sgokhales

35.2k26106140

asked Nov 9 '18 at 20:32

sgokhales

35.2k26106140

asked Nov 9 '18 at 20:32

sgokhales

35.2k26106140

Why don't you want to use a bag-of-words technique based on words/phrases that appear in such statements? It might work well! Similarly, some approach using word2vec/doc2vec embeddings might prove helpful – you'd have to try it. What have you tried so far? What kind of training dataset do you have, or expect to be able to create?
– gojomo
Nov 10 '18 at 0:54

add a comment |

Why don't you want to use a bag-of-words technique based on words/phrases that appear in such statements? It might work well! Similarly, some approach using word2vec/doc2vec embeddings might prove helpful – you'd have to try it. What have you tried so far? What kind of training dataset do you have, or expect to be able to create?
– gojomo
Nov 10 '18 at 0:54

Why don't you want to use a bag-of-words technique based on words/phrases that appear in such statements? It might work well! Similarly, some approach using word2vec/doc2vec embeddings might prove helpful – you'd have to try it. What have you tried so far? What kind of training dataset do you have, or expect to be able to create?
– gojomo
Nov 10 '18 at 0:54

add a comment |

1 Answer
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It seems what you are interested in doing is finding temporal statements in texts.

Not sure of your final output, but let's assume you want to find temporal phrases or sentences which contain them.

One methodology could be the following:

Create list of temporal terms [days, years, months, now, later]

Pick only sentences with key terms

Use sentences in doc2vec model

Infer vector and use distance metric for new sentence
- GMM Cluster + Limit
- Distance from average

Another methodology could be:

Create list of temporal terms [days, years, months, now, later]

Do Bigram and Trigram collocation extraction

Keep relevant collocations with temporal terms

Use relevant collocations in a kind of bag-of-collocations approach
- Matched binary feature vectors for relevant collocations
- Train classifier to recognise higher level text

This sounds like a good case for a Bootstrapping approach if you have large amounts of texts.

Both are semi-supervised really, since there is some need for finding initial temporal terms, but even that could be automated using a word2vec scheme and bootstrapping

answered Nov 10 '18 at 7:39

Nathan McCoy

1,1271125

add a comment |

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1 Answer
1

active

oldest

votes

1 Answer
1

active

oldest

votes

It seems what you are interested in doing is finding temporal statements in texts.

Not sure of your final output, but let's assume you want to find temporal phrases or sentences which contain them.

One methodology could be the following:

Create list of temporal terms [days, years, months, now, later]

Pick only sentences with key terms

Use sentences in doc2vec model

Infer vector and use distance metric for new sentence
- GMM Cluster + Limit
- Distance from average

Another methodology could be:

Create list of temporal terms [days, years, months, now, later]

Do Bigram and Trigram collocation extraction

Keep relevant collocations with temporal terms

Use relevant collocations in a kind of bag-of-collocations approach
- Matched binary feature vectors for relevant collocations
- Train classifier to recognise higher level text

This sounds like a good case for a Bootstrapping approach if you have large amounts of texts.

Both are semi-supervised really, since there is some need for finding initial temporal terms, but even that could be automated using a word2vec scheme and bootstrapping

answered Nov 10 '18 at 7:39

Nathan McCoy

1,1271125

add a comment |

It seems what you are interested in doing is finding temporal statements in texts.

Not sure of your final output, but let's assume you want to find temporal phrases or sentences which contain them.

One methodology could be the following:

Create list of temporal terms [days, years, months, now, later]

Pick only sentences with key terms

Use sentences in doc2vec model

Infer vector and use distance metric for new sentence
- GMM Cluster + Limit
- Distance from average

Another methodology could be:

Create list of temporal terms [days, years, months, now, later]

Do Bigram and Trigram collocation extraction

Keep relevant collocations with temporal terms

Use relevant collocations in a kind of bag-of-collocations approach
- Matched binary feature vectors for relevant collocations
- Train classifier to recognise higher level text

This sounds like a good case for a Bootstrapping approach if you have large amounts of texts.

Both are semi-supervised really, since there is some need for finding initial temporal terms, but even that could be automated using a word2vec scheme and bootstrapping

answered Nov 10 '18 at 7:39

Nathan McCoy

1,1271125

add a comment |

It seems what you are interested in doing is finding temporal statements in texts.

Not sure of your final output, but let's assume you want to find temporal phrases or sentences which contain them.

One methodology could be the following:

Create list of temporal terms [days, years, months, now, later]

Pick only sentences with key terms

Use sentences in doc2vec model

Infer vector and use distance metric for new sentence
- GMM Cluster + Limit
- Distance from average

Another methodology could be:

Create list of temporal terms [days, years, months, now, later]

Do Bigram and Trigram collocation extraction

Keep relevant collocations with temporal terms

Use relevant collocations in a kind of bag-of-collocations approach
- Matched binary feature vectors for relevant collocations
- Train classifier to recognise higher level text

This sounds like a good case for a Bootstrapping approach if you have large amounts of texts.

Both are semi-supervised really, since there is some need for finding initial temporal terms, but even that could be automated using a word2vec scheme and bootstrapping

answered Nov 10 '18 at 7:39

Nathan McCoy

1,1271125

It seems what you are interested in doing is finding temporal statements in texts.

Not sure of your final output, but let's assume you want to find temporal phrases or sentences which contain them.

One methodology could be the following:

Create list of temporal terms [days, years, months, now, later]

Pick only sentences with key terms

Use sentences in doc2vec model

Infer vector and use distance metric for new sentence
- GMM Cluster + Limit
- Distance from average

Another methodology could be:

Create list of temporal terms [days, years, months, now, later]

Do Bigram and Trigram collocation extraction

Keep relevant collocations with temporal terms

Use relevant collocations in a kind of bag-of-collocations approach
- Matched binary feature vectors for relevant collocations
- Train classifier to recognise higher level text

This sounds like a good case for a Bootstrapping approach if you have large amounts of texts.

Both are semi-supervised really, since there is some need for finding initial temporal terms, but even that could be automated using a word2vec scheme and bootstrapping

answered Nov 10 '18 at 7:39

Nathan McCoy

1,1271125

answered Nov 10 '18 at 7:39

Nathan McCoy

1,1271125

answered Nov 10 '18 at 7:39

Nathan McCoy

1,1271125

answered Nov 10 '18 at 7:39

Nathan McCoy

1,1271125

add a comment |

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