diff --git a/pysentence_similarity/_splitter.py b/pysentence_similarity/_splitter.py
new file mode 100644
index 0000000..a19694d
--- /dev/null
+++ b/pysentence_similarity/_splitter.py
@@ -0,0 +1,330 @@
+"""Splitter module."""
+import re
+import csv
+import json
+import logging
+from typing import List, Any, Union
+
+import requests
+from bs4 import BeautifulSoup
+
+# Logging configuration
+logger = logging.getLogger("pysentence-similarity:splitter")
+
+
+class Splitter:
+    """
+    A class to split text into sentences.
+    Supports splitting by periods, exclamation marks, question marks, and
+    newline characters.
+    """
+
+    def __init__(
+        self,
+        markers_to_split: Union[str, List[str]] = "\n",
+        preserve_markers: bool = False,
+    ) -> None:
+        """
+        Initializes the Splitter object.
+
+        :param markers_to_split: A string or list of characters (e.g., 
+        punctuation marks) used to split the text. Default is a newline 
+        character.
+        :type markers_to_split: Union[str, List[str]]
+        :param preserve_markers: A boolean indicating whether to include the 
+        split markers in the resulting text. Default is False.
+        :type preserve_markers: bool
+        """
+        if isinstance(markers_to_split, str):
+            markers_to_split = [markers_to_split]
+
+        if not isinstance(markers_to_split, list):
+            logger.error("Split list must be a list or tuple.")
+            raise ValueError("Split list must be a list or tuple.")
+
+        self.markers_to_split = markers_to_split
+        self.preserve_markers = preserve_markers
+        logger.info("Splitter initialized.")
+
+    def split_from_text(
+        self,
+        text: str,
+    ) -> List[str]:
+        """
+        Splits the given text into sentences based on punctuation and newlines.
+
+        :param text: The input text to split.
+        :type text: str
+        :return: A list of sentences.
+        :rtype: List[str]
+        """
+        try:
+            if not isinstance(text, str):
+                raise TypeError("Expected a string as input.")
+
+            if not text.strip():
+                logger.warning("Empty string provided for splitting.")
+                return []
+
+            regex_pattern = '|'.join(map(re.escape, self.markers_to_split))
+            if self.preserve_markers:
+                parts = re.split(f'({regex_pattern})', text)
+                sentences = [
+                    ''.join(
+                        part for part in parts[i:i + 2]
+                    ).strip() for i in range(0, len(parts)-1, 2)
+                ]
+            else:
+                sentences = re.split(regex_pattern, text)
+
+            sentences = [
+                sentence.strip() for sentence in sentences if sentence.strip()
+            ]
+            return sentences
+
+        except Exception as err:
+            logger.error(
+                "An error occurred while splitting the text: %s", err
+            )
+            raise
+
+    def split_from_file(
+        self,
+        file_path: str,
+    ) -> List[str]:
+        """
+        Splits the contents of a txt file into sentences based on punctuation
+        and newlines.
+
+        :param file_path: The path to the file to split.
+        :type file_path: str
+        :return: A list of sentences.
+        :rtype: List[str]
+        """
+        try:
+            if not isinstance(file_path, str):
+                raise TypeError("Expected a string as input.")
+
+            if not file_path.strip():
+                logger.warning("Empty string provided for splitting.")
+                return []
+            with open(file_path, 'r', encoding='utf-8') as file:
+                text = file.read()
+            return self.split_from_text(text)
+
+        except Exception as err:
+            logger.error(
+                "An error occurred while splitting the file: %s", err
+            )
+            raise
+
+    def split_from_url(
+        self,
+        url: str,
+        timeout: int = 10
+    ) -> List[str]:
+        """
+        Fetches the content from a URL, removes HTML tags, and splits the
+        cleaned text into sentences.
+
+        :param url: The URL of the webpage to split.
+        :type url: str
+        :param timeout: The number of seconds to wait for the request to
+        complete. Default is 10.
+        :type timeout: int
+        :return: A list of sentences.
+        :rtype: List[str]
+        """
+        try:
+            if not isinstance(url, str):
+                raise TypeError("Expected a string as input.")
+
+            if not isinstance(timeout, int):
+                raise TypeError("Expected an integer as input.")
+
+            response = requests.get(url, timeout=timeout)
+            response.raise_for_status()
+
+            # Use BeautifulSoup to clean the HTML
+            soup = BeautifulSoup(response.content, "html.parser")
+            text = soup.get_text()
+            return self.split_from_text(text)
+
+        except requests.exceptions.RequestException as req_err:
+            logger.error("An HTTP error occurred: %s", req_err)
+            raise
+        except Exception as err:
+            logger.error("An error occurred while processing the URL: %s", err)
+            raise
+
+    def split_from_csv(
+        self,
+        file_path: str,
+        column_names: List[str]
+    ) -> List[str]:
+        """
+        Reads a CSV file and splits the text from specified columns into
+        sentences.
+
+        :param file_path: The path to the CSV file to read.
+        :type file_path: str
+        :param column_names: A list of column names to extract text from.
+        :type column_names: List[str]
+        :return: A list of sentences extracted from the specified columns.
+        :rtype: List[str]
+        """
+        try:
+            if not isinstance(file_path, str):
+                raise TypeError("Expected a string as input for file_path.")
+
+            if not isinstance(column_names, list) or not all(
+                isinstance(col, str) for col in column_names
+            ):
+                raise TypeError(
+                    "Expected a list of strings as input for column_names."
+                )
+
+            if not file_path.strip():
+                logger.warning("Empty string provided for file_path.")
+                return []
+
+            sentences = []
+            with open(file_path, 'r', encoding='utf-8') as csvfile:
+                reader = csv.DictReader(csvfile)
+
+                if not reader.fieldnames:
+                    logger.error("No fieldnames found in the CSV file.")
+                    raise ValueError(
+                        "The CSV file is empty or has no headers."
+                    )
+
+                missing_columns = [
+                    col for col in column_names if col not in reader.fieldnames
+                ]
+                if missing_columns:
+                    logger.error(
+                        "Columns %s do not exist in the CSV file.",
+                        missing_columns
+                    )
+                    raise ValueError(
+                        f"Columns {missing_columns} do not exist."
+                    )
+
+                for row in reader:
+                    for column_name in column_names:
+                        text = row[column_name].strip()
+                        if text:
+                            sentences.extend(self.split_from_text(text))
+
+            return sentences
+
+        except FileNotFoundError:
+            logger.error("The specified file was not found: %s", file_path)
+            raise
+        except ValueError as val_err:
+            logger.error("Value error: %s", val_err)
+            raise
+        except Exception as err:
+            logger.error(
+                "An error occurred while processing the CSV file: %s", err
+            )
+            raise
+
+    def split_from_json(self, file_path: str, keys: List[str]) -> List[str]:
+        """
+        Reads a JSON file and splits text from specified keys into sentences.
+
+        :param file_path: The path to the JSON file to read.
+        :type file_path: str
+        :param keys: A list of keys to extract text from.
+        :type keys: List[str]
+        :return: A list of sentences extracted from the specified keys.
+        :rtype: List[str]
+        """
+        try:
+            if not isinstance(file_path, str):
+                raise TypeError("Expected a string as input for file_path.")
+
+            if not isinstance(keys, list) or not all(
+                isinstance(key, str) for key in keys
+            ):
+                raise TypeError(
+                    "Expected a list of strings as input for keys.")
+
+            if not file_path.strip():
+                logger.warning("Empty string provided for file_path.")
+                return []
+
+            sentences = []
+            with open(file_path, 'r', encoding='utf-8') as json_file:
+                data = json.load(json_file)
+                self._extract_json(data, keys, sentences)
+            return sentences
+
+        except FileNotFoundError:
+            logger.error("The specified file was not found: %s", file_path)
+            raise
+        except json.JSONDecodeError:
+            logger.error("Error decoding JSON from the file: %s", file_path)
+            raise
+        except Exception as err:
+            logger.error(
+                "An error occurred while processing the JSON file: %s", err)
+            raise
+
+    def _extract_json(
+        self,
+        data: Any,
+        keys: List[str],
+        sentences: List[str]
+    ) -> None:
+        """
+        Recursively extracts sentences from a nested JSON structure.
+
+        :param data: The JSON data to process (can be a dict, list, etc.).
+        :type data: Any
+        :param keys: A list of keys to extract text from.
+        :type keys: List[str]
+        :param sentences: A list to collect extracted sentences.
+        :type sentences: List[str]
+        """
+        if isinstance(data, dict):
+            for key in keys:
+                if key in data:
+                    text = data[key].strip()
+                    if text:
+                        sentences.extend(self.split_from_text(text))
+
+            for value in data.values():
+                self._extract_json(value, keys, sentences)
+
+        elif isinstance(data, list):
+            for item in data:
+                self._extract_json(item, keys, sentences)
+
+    def __str__(self) -> str:
+        """
+        Returns a string representation of the Splitter object.
+
+        :return: A string representation of the Splitter object.
+        :rtype: str
+        """
+        return (
+            f"Splitter(markers_to_split={self.markers_to_split}, "
+            f"preserve_markers={self.preserve_markers})"
+        )
+
+    def __repr__(self) -> str:
+        """
+        Returns a string representation of the Splitter object.
+
+        :return: A string representation of the Splitter object.
+        :rtype: str
+        """
+        return self.__str__()
+
+    def __copy__(self):
+        """Create a shallow copy of the SentenceSimilarity object."""
+        new_instance = self.__class__.__new__(self.__class__)
+        new_instance.__dict__.update(self.__dict__)
+        return new_instance