diff --git a/litstudy/__init__.py b/litstudy/__init__.py
index fcb125e..d96ce64 100644
--- a/litstudy/__init__.py
+++ b/litstudy/__init__.py
@@ -61,6 +61,7 @@
     build_corpus,
     train_nmf_model,
     train_lda_model,
+    train_elda_model,
     compute_word_distribution,
     calculate_embedding,
 )  # noqa: F401
diff --git a/litstudy/nlp.py b/litstudy/nlp.py
index b0486fa..b939e6e 100644
--- a/litstudy/nlp.py
+++ b/litstudy/nlp.py
@@ -311,16 +311,69 @@ def train_lda_model(corpus: Corpus, num_topics, seed=0, **kwargs) -> TopicModel:
 
     :param num_topics: The number of topics to train.
     :param seed: The seed used for random number generation.
-    :param kwargs: Arguments passed to `gensim.models.lda.LdaModel`.
+    :param kwargs: Arguments passed to `gensim.models.lda.LdaModel` (gensim3)
+                   or `gensim.models.ldamodel.LdaModel` (gensim4).
     """
-    from gensim.models.lda import LdaModel
 
     dic = corpus.dictionary
     freqs = corpus.frequencies
 
-    model = LdaModel(list(corpus), **kwargs)
+    from importlib.metadata import version
 
-    doc2topic = corpus2dense(model[freqs], num_topics)
+    gensim_mayor = int(version("gensim").split(".")[0])
+
+    if gensim_mayor == 3:
+        from gensim.models.lda import LdaModel
+
+        model = LdaModel(list(corpus), **kwargs)
+    elif gensim_mayor == 4:
+        from gensim.models.ldamodel import LdaModel
+
+        model = LdaModel(freqs, id2word=dic, num_topics=num_topics, **kwargs)
+    else:
+        from sys import exit
+
+        exit("LdaModel could not be imported from gensim 3 or 4.")
+
+    doc2topic = corpus2dense(model[freqs], num_topics).T
+    topic2token = model.get_topics()
+
+    return TopicModel(dic, doc2topic, topic2token)
+
+
+def train_elda_model(corpus: Corpus, num_topics, num_models=4, seed=0, **kwargs) -> TopicModel:
+    """Train a topic model using ensemble LDA.
+
+    :param num_topics: The number of topics to train.
+    :param num_models: The number of models to train.
+    :param seed: The seed used for random number generation.
+    :param kwargs: Arguments passed to `gensim.models.ensemblelda.EnsembleLda` (gensim4).
+    """
+
+    from importlib.metadata import version
+
+    gensim_mayor = int(version("gensim").split(".")[0])
+
+    if gensim_mayor <= 3:
+        from sys import exit
+
+        exit("EnsembleLda requires at least gensim 4.")
+
+    dic = corpus.dictionary
+    freqs = corpus.frequencies
+
+    from gensim.models.ensemblelda import EnsembleLda
+
+    model = EnsembleLda(
+        topic_model_class="ldamulticore",
+        corpus=freqs,
+        id2word=dic,
+        num_topics=num_topics,
+        num_models=num_models,
+        **kwargs
+    )
+
+    doc2topic = corpus2dense(model[freqs], num_topics).T
     topic2token = model.get_topics()
 
     return TopicModel(dic, doc2topic, topic2token)
diff --git a/litstudy/sources/scopus_csv.py b/litstudy/sources/scopus_csv.py
index 1ee3fad..2679f15 100644
--- a/litstudy/sources/scopus_csv.py
+++ b/litstudy/sources/scopus_csv.py
@@ -1,6 +1,7 @@
 """
 support loading Scopus CSV export.
 """
+
 from typing import List, Optional
 from ..types import Document, Author, DocumentSet, DocumentIdentifier, Affiliation
 from ..common import robust_open