The document presents research on using affect-enriched word embeddings to improve information retrieval from news datasets. Affect refers to feelings, emotions, personality and moods, which are important to capture for natural language understanding. Prior research showed that affect-enriched word embeddings outperformed the state-of-the-art on sentiment analysis, personality detection and frustration detection tasks. The document analyzes the affect scores of different news datasets and experiments with using affect-enriched embeddings for query expansion and document ranking, finding improvements over baselines.

1. Affect Enriched Word Embeddings for News
Information Retrieval
Tommaso Teofili, Niyati Chhaya
Adobe
{teofili,nchhaya}@adobe.com
July 25, 2019

2. Detecting affect in text
• Affect refers to the experience of feelings, emotions,
personality and moods
• An important aspect to capture for natural language
understanding
• Applications:
• Analysing consumer behaviour
• Opinion mining
• Sentiment analysis

3. Affect enriched word embedding models (S Khosla, N
Chhaya, K Chawla @ COLING 2018)
• Affect-enriched word distributions trained on Warriner’s
lexicon coupled with ”plain” word embeddings
• FPF Enron dataset
• Beat SotA in
• Intrinsic word similarity
• Sentiment analysis
• Personality detection
• Frustration detection in interpersonal communication

4. Aff2vec embeddings in News IR - why ?
• More resilient to synonym / antonym issue
• Analysis of affect score in news datasets
Dataset affect scoring
Dataset formality politeness frustration
NYT 0.7087 0.6291 0.6248
WP 0.7788 0.7456 0.6510
CACM 0.3619 0.1229 0.3511
ClueWeb09 0.4319 0.2708 0.6216
Table: Mean affect scores on some common IR datasets

5. Query expansion with word embeddings

6. Ranking with average word embeddings

7. TF-iDF average word embeddings

8. Query expansion experiments - NYT dataset
Model MAP NDCG
BM25 0.1977 0.4334
QL 0.1913 0.4325
GloVe 0.1951 0.4337
w2v-GoogleNews-300 0.1947 0.4308
dict2vec-dim100 0.1995 0.4335
WordNet 0.1977 0.4334
GloVe-retrofitted 0.1940 0.4264
paragram-retrofitted 0.1798 0.4012
w2v-retrofitted 0.1967 0.4368
GloVe-affect 0.1947 0.4308
GloVe-retrofitted-affect-555 0.2021 0.4421
paragram-affect 0.1977 0.4309
w2v-affect 0.1940 0.4305
w2v-retrofitted-affect 0.1971 0.4345

9. Ranking experiments - NYT dataset
Model NDCG MAP
BM25 0.4334 0.1977
QL 0.4325 0.1913
GloVe.6B.300d 0.4316 0.1946
w2v-GoogleNews-300 0.4294 0.1922
dict2vec-dim200 0.4155 0.1891
GloVe-retrofitted 0.4216 0.1861
paragram-74627 0.4337 0.1937
paragram-retrofitted 0.3969 0.1703
w2v-retrofitted 0.4341 0.1914
counterfit-GloVe-affect 0.4311 0.1753
GloVe-affect 0.4594 0.1926
GloVe-retrofitted-affect-555 0.4693 0.1948
paragram-affect 0.4619 0.1969
w2v-affect 0.4592 0.1926
w2v-retrofitted-affect 0.4601 0.1911