This project provides executable files and scripts for generating sentiment lexicons from GermaNet (a German equivalent of the English WordNet), raw text corpora, and neural word embeddings.

For generating a sentiment lexcion from pre-trained word embeddings, you first need to compile the C++ code by running the following commands (please note that the build requires the Armadillo library to be installed):

cd build/
cmake ../
make

Afterwards, an executable called vec2dic wil apper in the subdirectory bin. You can exectute this file by envoking:

./bin/vec2dic [OPTIONS] --type=TYPE VECTOR_FILE SEED_FILE

where the TYPE argument (an integer from zero to three) will determine the algorithm to use for inducing a sentiment lexicon, VECTORE_FILE denotes a path to a text file with pre-trained word2vec embeddings (note that the file should be in the raw text format with space separated values), and SEED_FILE. We currently support the following types of algorithms:

• 0 -- nearest centroids (default);
• 1 -- KNN;
• 2 -- PCA.

In addition to the C++ executables, we also provide several reimplementations of popular alternative approaches which generate sentiment lexcions from lexical taxonomies (e.g., GermaNet) or raw unlabeled text corpora. Please note that in order to use dictionary-based methods, you need to download GermaNet, which is not included here by default due to license restrictions, and place its files in the directory data/GermaNet_v9.0/. For corpus-based algorithms, you need to provide a pre-lemmatized corpus in the format similar to the one used in data/snapshot_corpus_data/example.txt. Alternatively, for the method of Takamura et al. (2005), you need to provide a list of coordinately conjoined pairs similar to the one provided in data/corpus/cc_light.txt.

Below, you can find a short summary and command examples of the provided systems.

For generating a sentiment lexicon with the method of Hu and Liu (2004), you should envoke the following command:

./scripts/generate_lexicon.py hu-liu \
 --ext-syn-rels --seed-pos=adj \
--form2lemma=data/GermaNet_v9.0/gn_form2lemma.txt \
data/seeds/hu_liu_seedset.txt data/GermaNet_v9.0

If you want to generate a sentiment lexicon using the method of Blair-Goldensohn et al. (2008), you should envoke the following command:

./scripts/generate_lexicon.py blair-goldensohn \
 --ext-syn-rels --seed-pos=adj \
 --form2lemma=data/GermaNet_v9.0/gn_form2lemma.txt \
 data/seeds/hu_liu_seedset.txt data/GermaNet_v9.0/

For generating a sentiment lexicon with the method of Kim and Hovy, (2004), use the following command:

./scripts/generate_lexicon.py kim-hovy \
 --ext-syn-rels --seed-pos=adj \
 --form2lemma=data/GermaNet_v9.0/gn_form2lemma.txt \
 data/seeds/hu_liu_seedset.txt data/GermaNet_v9.0/

To generate a sentiment lexicon with the method of Takamura et al. (2005), use the following command instead (note that the file data/corpus/cc.txt is not included in this repository due to its big size):

./scripts/generate_lexicon.py takamura \
    --form2lemma=data/GermaNet_v9.0/gn_form2lemma.txt \
    data/seeds/turney_littman_2003.txt data/GermaNet_v9.0/ data/corpus/cc.txt -1

For generating a sentiment lexicon using the SentiWordNet method of Esuli and Sebastiani (2006), you should use the following command:

./scripts/generate_lexicon.py esuli --ext-syn-rels \
--seed-pos=adj --form2lemma=data/GermaNet_v9.0/gn_form2lemma.txt \
data/seeds/hu_liu_seedset.txt data/GermaNet_v9.0

In order to generate a sentiment lexicon with the min-cut approach of Rao and Ravichandran (2009), use the below command:

./scripts/generate_lexicon.py rao-min-cut --ext-syn-rels \
--seed-pos=adj --form2lemma=data/GermaNet_v9.0/gn_form2lemma.txt \
data/seeds/hu_liu_seedset.txt data/GermaNet_v9.0

If you want to test the label propagation algorithm described by these authors, you should specify the following arguments:

./scripts/generate_lexicon.py rao-lbl-prop --ext-syn-rels \
--seed-pos=adj --form2lemma=data/GermaNet_v9.0/gn_form2lemma.txt \
data/seeds/hu_liu_seedset.txt data/GermaNet_v9.0

To generate a sentiment lexicon using the method of Awdallah and Radev (2010), you should use the following command:

./scripts/generate_lexicon.py awdallah --ext-syn-rels \
--seed-pos=adj --form2lemma=data/GermaNet_v9.0/gn_form2lemma.txt \
data/seeds/hu_liu_seedset.txt data/GermaNet_v9.0/

For generating a sentiment lexicon using the algorithm of Velikovich et al. (2010), you can use the following command:

./scripts/generate_lexicon.py velikovich \
data/seeds/hu_liu_seedset.txt -1 data/snapshot_corpus_data/example.txt

In order to generate a sentiment lexicon using the system of Kiritchenko et al. (2014), you should use the following command:

./scripts/generate_lexicon.py kiritchenko \
data/seeds/hu_liu_seedset.txt -1 data/snapshot_corpus_data/example.txt

For generating a sentiment lexicon using the approach of Severyn and Moschitti (2014), you should use the following command:

./scripts/generate_lexicon.py severyn \
data/seeds/hu_liu_seedset.txt -1 data/snapshot_corpus_data/example.txt

You can evaluate the resulting sentiment lexicon on the PotTS dataset by using the following command and providing a valid path to the downloaded corpus data:

./scripts/evaluate.py -l data/form2lemma.txt \
	data/results/esuli-sebastiani/esuli-sebastiani.ext-syn-rels.turney-littman-seedset.txt \
	${PATH_TO_PotTS}/corpus/basedata/ ${PATH_TO_PotTS}/corpus/annotator-2/markables/