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-# topic-analysis
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+[![SingnetLogo](docs/assets/singnet-logo.jpg?raw=true 'SingularityNET')](https://singularitynet.io/)
+
+# Topic Analysis Services
+
+
+This repository contains various [topic analysis](https://en.wikipedia.org/wiki/Topic_model) services for SingularityNET. The topic analysis methods would include:
+
+* Latent semantic analysis (LSA)
+* Probabilistic latent semantic analysis (PLSA)
+* Latent Diritchlet allocation (LDA)
+* LDA2vec
+
+The services are wrapped using gRPC.
+
+The user provides a collection of documents for topic analysis and the service would return discoverd topics. Each topic
+consists of a collection of words that represent a given topic.
+
+
+## Resources
+
+LSA:
+ * Research paper: [Indexing by Latent Semantic Analysis](http://citeseer.ist.psu.edu/viewdoc/download?doi=10.1.1.108.8490&rep=rep1&type=pdf)
+ * [Wikipedia entry](https://en.wikipedia.org/wiki/Latent_semantic_analysis)
+
+PLSA:
+ * Research aper: [Unsupervised Learning by Probabilistic Latent Semantic Analysis](http://www.cs.bham.ac.uk/~pxt/IDA/plsa.pdf)
+ * [Wikipedia entry](https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis)
+
+LDA:
+ * Research paper: [Latent Dirichlet Allocation](http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf)
+ * [Wikipedia entry](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation)
+
+LDA2vec:
+ * Research paper: [Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec](https://arxiv.org/abs/1605.02019)
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