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+# DEFT 2017 - Sentiment Analysis
+
+- Authors: Mickael Rouvier and Pierre-Michel Bousquet
+- Version: 1.0
+- Date: 26/06/17
+
+These scripts provide the LIA system that I used for the DEFT 2017 - Sentiment Analysis. The LIA system is a multi-view ensemble of Convolutional Neural Networks (CNN). Four different word embeddins are used to initialize the input of CNN : lexical embedding, sentiment embedding (multi-task learning), sentiment embedding (distant learning) and sentiment embedding (negative sampling). The system is a fusion at the score level of the different CNNs variants.
+
+
+You can reproduce my results or freely adapt my code for your experiments.
+
+
+Warning, before to run the system execute the makefile:
+
+```shell
+make
+```
+
+This executable split the training corpus (K-Fold) and tokenize the tweets:
+
+```shell
+sh run_corpus.sh
+```
+
+This executable train the different word embeddings:
+```shell
+sh run_word2vec.sh
+```
+
+This executable learn the models:
+
+```shell
+sh run_cnn.sh
+```
+
+This executable run the model on dev and test:
+
+```shell
+sh run_extract_dev.sh
+sh run_extract_test.sh
+```
+
+At this point you can score the CNNs:
+```shell
+ruby bin/scoring.rb data/task1_test.tokenize  results_test/cnn_task1_0_distant_size100_123.txt
+ruby bin/scoring.rb data/task2_test.tokenize  results_test/cnn_task2_0_distant_size100_123.txt
+ruby bin/scoring.rb data/task3_test.tokenize  results_test/cnn_task3_0_distant_size100_123.txt
+```
+
+
+This executable run the fusion system:
+```shell
+sh run_fusion.sh
+```
+
+
+Finally, you can score the full-system:
+```shell
+ruby bin/scoring.rb data/task1_test.tokenize  output/equipe-8_tache1_run3.csv
+ruby bin/scoring.rb data/task2_test.tokenize  output/equipe-8_tache2_run1.csv
+ruby bin/scoring.rb data/task3_test.tokenize  output/equipe-8_tache3_run3.csv
+```
+
+
+
+# Results
+
+
+## Baseline
+
+We reproduce the sentiment analysis system of Kim (based on Word embeddings and CNN):
+
+
+| Corpus       | Baseline      | 
+| ------------ |:-------------:|
+| Task1        |  59.55        |
+| Task2        |  77.18        |
+| Task3        |  57.59        |
+
+
+
+
+## DEFT 2017
+
+These results are those SENSEI-LIF system presented in SemEval 2016 Sentiment Analysis:
+
+| Corpus      | Task1         | Task2         | Task3         |
+| ----------- |:-------------:|:-------------:|:-------------:|
+| Run1        |  60.23        |  78.31        |  57.83        |
+| Run2        |  63.44        |  77.39        |  58.49        |
+| Run3        |  65.00        |  77.43        |  59.39        |
+
+
+# Citing
+
+The system is described in this paper:
+
+    @inproceedings{rouvier2017,
+      author    = {Mickael Rouvier and Pierre-Michel Bousquet},
+      title     = {LIA @ DEFT’2017 : Multi-view Ensemble of Convolutional Neural Network},
+      booktitle = {DEFT 2107},
+      year      = {2017},
+      address   = {Orleans, France}
+    }
+
+