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KSenticNet: 한국어 감성 사전

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# KSenticNet: 한국어 감성 사전 (Korean sentiment resource)

### How to use
- Just download 'ksenticnet_kaist.py' file :)

### Overview
- There are several Korean sentiment analysis resources such as [KNU SentiLex](https://github.com/park1200656/KnuSentiLex), [KOSAC](http://word.snu.ac.kr/kosac/).
- However, sentiment lexicons like them require a lot of time and human resources.
- So I decided to make it easier and automated by combining [SenticNet](https://sentic.net/) and KAIST Korean wordnet([KWN](http://wordnet.kaist.ac.kr/)).

### Example Image
![KSenticNet Example](https://raw.githubusercontent.com/zzaebok/ksenticnet/master/KSenticnetExample.png)
- You can get words' sentic values, sentiments, polarity value and semantics.
- I recommend you to use it with POS tagger(such as Kkma).

### Building Process
![KSenticNet Structure](https://raw.githubusercontent.com/zzaebok/ksenticnet/master/KsenticNetStructure.png)
> #### Features
- It follows major process of [CSenticNet](https://sentic.net/csenticnet.pdf).
- But it resolved duplicated sentic value problem on Korean and English word.
> #### Process
1. Make {english word : synsets} dictionary through KWN.
2. Direct mapping ( Compare each synset's hypernyms to semantics in SenticNet words and find pair )
3. Apply Lesk algorithm to the non-matched words in SenticNet.
4. During 2, 3 there are synsets which get several different sentic values. Apply weighted average on sentic values based on AffectNet frequencies.
5. For Korean words, assign the sentic value which was assigned on the synset.
6. During 5, there are synsets which have only one Korean word. For those, use weighted average sentic value same as process 4.
7. During 5, several Korean words are assigned different sentic values but we cannot use weighted average because each synset contains multiple Korean words. So compute average cosine similarity * of synsets for that Korean word and use only the most adequate synset to give sentic value.

\* Cosine similarity is computed from Korean tuned-embedding vectors. The vectors of Korean words are tuned by [Context2Vec structure](https://github.com/SenticNet/context2vec) from facebook Fasttext. In this structure, I scraped example sentences for target words from several dictionaries. While applying Bi-LSTM, Self-Attention, Neural Tensor Network, pre-trained Fasttext vectors are modified and adjusted. By using these tuned vectors we can compute cosine similarities among other Korean words in a synset and use average similarity as an index of 'adequacy'.

### Resources
- SenticNet5
- CSenticNet
- [Korean Fasttext word embeddings](https://fasttext.cc/docs/en/pretrained-vectors.html)
- KAIST Korean Wordnet
- [연세 한국어 사전](https://ilis.yonsei.ac.kr/ysdic/), 표준 국어 대사전, 고려대 한국어 대사전, 우리말샘

### Results and validation
- We can assign sentic value to 5465 Korean words.
- Validate it through 1000 positive reviews and 1000 negative reviews in [NAVER movie review corpus](https://github.com/e9t/nsmc) ( simple count after tokenizing by [Kkma](https://github.com/konlpy/kkma) )
- Precision: 52.87% | Recall: 85.4% | F1: 65.31%

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