NoVAD added

README.md

@@ -50,20 +50,21 @@ The predefined lexicon identifiers follow the convention {NAME}_{VERSION} - for
 
 See below for the available predefined lexicon identifier.
 
-| Sentiment Dictionary | Associated Institution <br> (Principal Investigator) | Description | Genre | Domain | Predefined Identifiers (preprocessed) |
-|------------------------|---------------------|---------------|------|-----|------------------------|
-|**AFINN** <br> (Nielsen, 2011)| DTU Informatics <br> (Technical University of Denmark) | General purpose lexicon with sentiment ratings for common emotion words. |Social Media|General| `AFINN_v2009`, `AFINN_v2011`, `AFINN_v2015` |
-|**Aigents+** <br> (Raheman et al., 2022)| Autonio Foundation | Lexicon optimised for social media posts related to cryptocurrencies. |Social Media|Cryptocurrency| `Aigents+_v2022`|
-|**ANEW** <br> (Bradley and Lang, 1999)| NIMH Center for Emotion and Attention <br> (University of Florida) | Provides normative emotional ratings across pleasure, arousal, and dominance dimensions.|General|Psychology|`ANEW_v1999_simple`, `ANEW_v1999_weighted`|
-|**Dictionary of Affect in Language (DAL)** <br> (Whissell, 1989; Whissell, 2009)| Laurentian University | Lexicon designed to quantify pleasantness, activation, and imagery dimensions across diverse everyday English words. | General | General | `DAL_v2009_norm`, `DAL_v2009_boosted`|
-|**Discrete Emotions Dictionary (DED)** <br> (Fioroni et al., 2022)| Gallup | Lexicon focused on precisely distinguishing four key discrete emotions in political communication | News | Political Science | `DED_v2022` |
-|**General Inquirer** <br> (Stone et al., 1962)| Harvard University | Lexicon capturing broad psycholinguistic dimensions across semantics, values and motivations.  |General|Psychology, Political Science| `HarvardGI_v2000`|
-|**Henry** <br> (Henry, 2006) | University of Miami  | Leixcon designed for analysing tone in earnings press releases. |Corporate Communication (Earnings Press Releases)|Finance| `Henry_v2006`|
-|**MASTER** <br> (Loughran and McDonland, 2011; Bodnaruk, Loughran and McDonald, 2015)| University of Notre Dame | Financial lexicons covering expressions common in business writing. |Regulatory Filings (10-K)|Finance| `MASTER_v2022`|
-|**OpinionLexicon** <br> (Hu and Liu, 2004)| University of Illinois Chicago | Opinion words tailored for sentiment analysis of product reviews.|Product Reviews|Consumer Products|`OpinionLexicon_v2004`|
-|**SentiWordNet** <br> (Esuli and Sebastiani, 2006; Baccianella, Esuli and Sebastiani, 2010)| Institute of Information Science and Technologies <br>(Consiglio Nazionale delle Ricerche) | Lexicon associating WordNet synsets with positive, negative, and objective scores. |General|General| `SentiWordNet_v2010_simple`, `SentiWordNet_v2010_nuanced` |
-|**VADER** <br> (Hutto and Gilbert, 2014)| Georgia Institute of Technology | General purpose lexicon optimised for social media and microblogs. |Social Media|General| `VADER_v2014`|
-|**WordNet-Affect** <br> (Strapparava and Valitutti, 2004; Valitutti, Strapparava and Stock, 2004; Strapparava, Valitutti and Stock, 2006)| Institute for Scientific and Technological Research <br> (Fondazione Bruno Kessler) | Hierarchically organised affective labels providing a  granular emotional dimension. |General|Psychology| `WordNet-Affect_v2006`|
+| Sentiment Dictionary | Description | Genre | Domain | Predefined Identifiers (preprocessed) |
+|------------------------|---------------|------|-----|------------------------|
+|**AFINN** <br> (Nielsen, 2011)| General purpose lexicon with sentiment ratings for common emotion words. |Social Media|General| `AFINN_v2009`, `AFINN_v2011`, `AFINN_v2015` |
+|**Aigents+** <br> (Raheman et al., 2022)| Lexicon optimised for social media posts related to cryptocurrencies. |Social Media|Cryptocurrency| `Aigents+_v2022`|
+|**ANEW** <br> (Bradley and Lang, 1999)| Provides normative emotional ratings across pleasure, arousal, and dominance dimensions.|General|Psychology|`ANEW_v1999_simple`, `ANEW_v1999_weighted`|
+|**Dictionary of Affect in Language (DAL)** <br> (Whissell, 1989; Whissell, 2009)| Lexicon designed to quantify pleasantness, activation, and imagery dimensions across diverse everyday English words. | General | General | `DAL_v2009_boosted`, `DAL_v2009_norm` |
+|**Discrete Emotions Dictionary (DED)** <br> (Fioroni et al., 2022)| Lexicon focused on precisely distinguishing four key discrete emotions in political communication | News | Political Science | `DED_v2022` |
+|**General Inquirer** <br> (Stone et al., 1962)| Lexicon capturing broad psycholinguistic dimensions across semantics, values and motivations.  |General|Psychology, Political Science| `HarvardGI_v2000`|
+|**Henry** <br> (Henry, 2006) | Leixcon designed for analysing tone in earnings press releases. |Corporate Communication (Earnings Press Releases)|Finance| `Henry_v2006`|
+|**MASTER** <br> (Loughran and McDonland, 2011; Bodnaruk, Loughran and McDonald, 2015)| Financial lexicons covering expressions common in business writing. |Regulatory Filings (10-K)|Finance| `MASTER_v2022`|
+|**Norms of Valence, Arousal and Dominance (NoVAD)** <br> (Warriner, Kuperman and Brysbaert, 2013; Warriner and Kuperman, 2014)| A lexicon of 14,000 common English lemmas across valence, arousal, and dominance dimensions.  | General | Psychology |  `NoVAD_v2013_adjusted`, `NoVAD_v2013_bidimensional`|
+|**OpinionLexicon** <br> (Hu and Liu, 2004)| Opinion words tailored for sentiment analysis of product reviews.|Product Reviews|Consumer Products|`OpinionLexicon_v2004`|
+|**SentiWordNet** <br> (Esuli and Sebastiani, 2006; Baccianella, Esuli and Sebastiani, 2010)| Lexicon associating WordNet synsets with positive, negative, and objective scores. |General|General| `SentiWordNet_v2010_logtransform`, `SentiWordNet_v2010_simple`|
+|**VADER** <br> (Hutto and Gilbert, 2014)| General purpose lexicon optimised for social media and microblogs. |Social Media|General| `VADER_v2014`|
+|**WordNet-Affect** <br> (Strapparava and Valitutti, 2004; Valitutti, Strapparava and Stock, 2004; Strapparava, Valitutti and Stock, 2006)| Hierarchically organised affective labels providing a  granular emotional dimension. |General|Psychology| `WordNet-Affect_v2006`|
 
 Refer [documentation](docs_link) for details on usage.
 

doc/licenses.txt

@@ -18,7 +18,8 @@ https://github.com/aigents/aigents-java
 
 #ANEW
 
-?
+Publicly Avaliable 
+(Report)
 
 
 #DED (Discrete Emotions Dictionary)
@@ -27,10 +28,13 @@ https://osf.io/cbm9e/
 
 https://github.com/CenterForOpenScience/cos.io/blob/master/TERMS_OF_USE.md
 
+Publicly Avaliable 
+(Open Science Framework) 
 
-#Dictionary of Affect 
 
-? 
+#DAL (Dictionary of Affect in Language)
+
+Publicly Avaliable 
 
 
 #General Inquirer 
@@ -44,7 +48,8 @@ https://inquirer.sites.fas.harvard.edu/spreadsheet_guide.htm
 
 #Henry 
 
-Unspecified 
+Publicly avaliable 
+(SSRN)
 
 
 #MASTER 
@@ -54,11 +59,6 @@ For commercial licenses, please contact us at loughranmcdonald@gmail.com.
 https://sraf.nd.edu/loughranmcdonald-master-dictionary/
 
 
-#MPQA 
-
-? 
-
-
 #News PMI 
 
 ? 
@@ -87,6 +87,7 @@ https://github.com/consose/SentiBigNomics
 
 #SenticNet 
 
+MIT License 
 https://sentic.net/downloads/
 
 
@@ -120,9 +121,6 @@ You are free to share and adapt the lexicon, as long as you give attribution to
 https://wndomains.fbk.eu/download.html 
 
 
-
-
-
 #EmoLex 
 
 Custom License 
@@ -170,4 +168,33 @@ https://arxiv.org/abs/1406.0032
 
 
 #SentiStrength 
-http://sentistrength.wlv.ac.uk/ 
+http://sentistrength.wlv.ac.uk/ 
+
+
+#MPQA 
+
+The annotations in this data collection are copyrighted by the MITRE Corporation. 
+User acknowledges and agrees that: 
+(i) as between User and MITRE, MITRE owns all the right, title and interest in the Annotated Content, unless expressly stated otherwise; 
+(ii) nothing in this Agreement shall confer in User any right of ownership in the Annotated Content; and 
+(iii) User is granted a non-exclusive, royalty free, worldwide licence (with no right to sublicense) to use the Annotated Content solely for academic and research purposes. 
+This Agreement is governed by the law of the Commonwealth of Massachusetts and User agrees to submit to the exclusive jurisdiction of the Massachusetts courts.
+
+
+#+/-Effect Lexicon
+
+GNU General Public License 
+
+
+#Warriner 
+
+Publicly avaliable 
+(Springer)
+https://www.springernature.com/gp/authors/research-data-policy/data-availability-statements
+
+
+#HSSWE 
+
+https://github.com/NUSTM/HSSWE/tree/master
+https://aclanthology.org/D17-1052/
+CC BY 4.0 DEED 

sentibank/archive.py

@@ -102,9 +102,9 @@ def dict(self, idx: str):
             with open(file_path, "rb") as handle:
                 self.lex_dict = pickle.load(handle)
 
-        elif idx == "SentiWordNet_v2010_nuanced": 
+        elif idx == "SentiWordNet_v2010_logtransform": 
             file_path = os.path.join(
-                self.script_dir, "dict_arXiv", "SentiWordNet", "SentiWordNet_v2010_nuanced.pickle"
+                self.script_dir, "dict_arXiv", "SentiWordNet", "SentiWordNet_v2010_logtransform.pickle"
             )
             with open(file_path, "rb") as handle:
                 self.lex_dict = pickle.load(handle)
@@ -157,6 +157,20 @@ def dict(self, idx: str):
             )
             with open(file_path, "rb") as handle:
                 self.lex_dict = pickle.load(handle)
+
+        elif idx == "NoVAD_v2013_bidimensional": 
+            file_path = os.path.join(
+                self.script_dir, "dict_arXiv", "NoVAD", "NoVAD_v2013_bidimensional.pickle"
+            )
+            with open(file_path, "rb") as handle:
+                self.lex_dict = pickle.load(handle)
+
+        elif idx == "NoVAD_v2013_adjusted": 
+            file_path = os.path.join(
+                self.script_dir, "dict_arXiv", "NoVAD", "NoVAD_v2013_adjusted.pickle"
+            )
+            with open(file_path, "rb") as handle:
+                self.lex_dict = pickle.load(handle)
 
         else: 
             raise ValueError
@@ -261,7 +275,13 @@ def origin(self, idx: str):
                 self.script_dir, "dict_arXiv", "DAL", "DAL_v2009.csv"
             )
             self.origin_df = pd.read_csv(file_path)
-
+
+        elif idx == "NoVAD_v2013": 
+            file_path = os.path.join(
+                self.script_dir, "dict_arXiv", "NoVAD", "NoVAD_v2013.csv"
+            )
+            self.origin_df = pd.read_csv(file_path)
+
         else:
             raise ValueError